Emerging patterns based classifier

Contrast Data Mining: Methods
and Applications
James Bailey, NICTA Victoria Laboratory
and The University of Melbourne
Guozhu Dong, Wright State University

Presented at the IEEE International Conference on Data Mining (ICDM), October 28-31 2007
An up to date version of this tutorial is available at http://www.csse.unimelb.edu.au/~jbailey/contrast

Contrast data mining - What is it ?

Contrast - ``To compare or appraise in
respect to differences’’ (Merriam Webster Dictionary)

Contrast data mining - The mining of
patterns and models contrasting two or
more classes/conditions.

Contrast Data Mining: Methods and Applications
James Bailey and Guozhu Dong IEEE ICDM 28-31 Oct. 07 2

Contrast Data Mining - Why ?
``Sometimes it’s good to contrast what you
like with something else. It makes you
appreciate it even more’’
Darby Conley, Get Fuzzy, 2001


What can be contrasted ?
Objects at different time periods
``Compare ICDM papers published in 2006-2007
versus those in 2004-2005’’
Objects for different spatial locations
``Find the distinguishing features of location x for
human DNA, versus location x for mouse DNA’’
Objects across different classes
``Find the differences between people with
brown hair, versus those with blonde hair’’


What can be contrasted ? Cont.
Objects within a class
``Within the academic profession, there are few
people older than 80’’ (rarity)
``Within the academic profession, there are no rich
people’’ (holes)
``Within computer science, most of the papers come
from USA or Europe’’ (abundance)
Object positions in a ranking
``Find the differences between high and low income earners’’
Combinations of the above


Alternative names for contrast data
mining
Contrast={change, difference, discriminator,
classification rule, …}

Contrast data mining is related to topics such
as:
Change detection, class based association rules, contrast sets,
concept drift, difference detection, discriminative patterns,
(dis)similarity index, emerging patterns, gradient mining, high
confidence patterns, (in)frequent patterns, top k patterns,……


Characteristics of contrast data
mining
Applied to multivariate data
Objects may be relational, sequential,
graphs, models, classifiers, combinations
of these
Users may want either
To find multiple contrasts (all, or top k)
A single measure for comparison
• ``The degree of difference between the groups (or
models) is 0.7’’

Contrast characteristics Cont.
Representation of contrasts is important.
Needs to be
Interpretable, non redundant, potentially actionable,
expressive
Tractable to compute
Quality of contrasts is also important. Need
Statistical significance, which can be measured in
multiple ways
Ability to rank contrasts is desirable, especially for
classification


How is contrast data mining used ?
Domain understanding
``Young children with diabetes have a greater risk of hospital
admission, compared to the rest of the population
Used for building classifiers
Many different techniques - to be covered later
Also used for weighting and ranking instances
Used in construction of synthetic instances
Good for rare classes
Used for alerting, notification and monitoring
``Tell me when the dissimilarity index falls below 0.3’’


Goals of this tutorial
Provide an overview of contrast data
mining
Bring together results from a number of
disparate areas.
Mining for different types of data
• Relational, sequence, graph, models, …
Classification using discriminating patterns


By the end of this tutorial you will
be able to …
Understand some principal techniques for
representing contrasts and evaluating
their quality
Appreciate some mining techniques for
contrast discovery
Understand techniques for using
contrasts in classification


Don’t have time to cover ..
String algorithms
Connections to work in inductive logic
programming
Tree-based contrasts
Changes in data streams
Frequent pattern algorithms
Connections to granular computing
…


Outline of the tutorial
Basic notions and univariate contrasts
Pattern and rule based contrasts
Contrast pattern based classification
Contrasts for rare class datasets
Data cube contrasts
Sequence based contrasts
Graph based contrasts
Model based contrasts
Common themes + open problems + summary

Basic notions and univariate case
Feature selection and feature significance
tests can be thought of as a basic
contrast data mining activity.
``Tell me the discriminating features’’
• Would like a single quality measure
• Useful for feature ranking
Emphasis is less on finding the contrast and
more on evaluating its power


Sample Feature-Class Dataset
ID Height (cm) Class
9004 150 Happy ☺
1005 200 Sad
9006 137 Happy ☺
4327 120 Happy ☺
3325 …… …..

Discriminative power
Can assess discriminative power of Height
feature by
Information measures (signal to noise, information
gain ratio, …)
Statistical tests (t-test, Kolmogorov-Smirnov, Chi
squared, Wilcoxon rank sum, …). Assessing
whether
• The mean of each class is the same
• The samples for each class come from the same distribution
• How well a dataset fits a hypothesis

No single test is best in all situations !

Example Discriminative Power
Test - Wilcoxon Rank Sum
Suppose n1 happy, and n2 sad instances
Sort the instances according to height value:
h1 <= h2 <= h3 <= … hn1+n2
Assign a rank to each instance, indicating how many
instances in the other class are less. For x in class A
Rank(x)=|{y: class(y)<>A and height(y)<height(x)}|
For each class
Compute the Ranksum=Sum(ranks of all its instances)
Null Hypothesis: The instances are from the same distribution
Consult statistical significance table to determine whether value
of Ranksum is significant


Rank Sum Calculation Example
ID Height(cm) Class Rank
324 220 Happy ☺ 3
481 210 Sad 2
660 190 Sad 2
321 177 Happy ☺ 1
415 150 Sad 1
816 120 Happy ☺ 0
Happy: RankSum=3+1+0=4 Sad:RankSum=2+2+1=5


Wilcoxon Rank Sum TestCont.
Non parametric (no normal distribution assumption)
Requires an ordering on the attribute values
Scaled value of Ranksum is equivalent to area under ROC curve
for using the selected feature as a classifier
100%

Ranksum
(n1*n2)

True Positive
Rate
0
%
0 100
% False Positive %
Rate

Discriminating with attribute values
Can alternatively focus on significance of
attribute values, with either
1) Frequency/infrequency (high/low counts)
Frequent in one class and infrequent in the other.
• There are 50 happy people of height 200cm and only 2 sad
people of height 200cm
2) Ratio (high ratio of support)
Appears X times more in one class than the other
• There are 25 times more happy people of height 200cm
than sad people of height 200cm


Attribute/Feature Conversion
Possible to form a new binary feature
based on attribute value and then apply
feature significance tests
Blur distinction between attribute and
attribute value
150cm 200cm … Class
Yes No … Happy ☺
No Yes … Sad

Discriminating Attribute Values in a
Data Stream
Detecting changes in attribute values is an
important focus in data streams
Often focus on univariate contrasts for efficiency
reasons
Finding when change occurs (non stationary
stream).
Finding the magnitude of the change. E.g. How big is
the distance between two samples of the stream ?
Useful for signaling necessity for model update or an
impending fault or critical event


Odds ratio and Risk ratio
Can be used for comparing or measuring
effect size
Useful for binary data
Well known in clinical contexts
Can also be used for quality evaluation of
multivariate contrasts (will see later)
A simple example given next


Odds and risk ratio Cont.

ID Gender Exposed
(feature) (event)
1 Male Yes
2 Female No
3 Male No
4 … …


Odds Ratio Example
Suppose we have 100 men and 100 women,
and 70 men and 10 women have been exposed
Odds of exposure(male)=0.7/0.3=2.33
Odds of exposure(female)=0.1/0.9=0.11
Odds ratio=2.33/.11=21.2
Males have 21.2 times the odds of exposure
than females
Indicates exposure is much more likely for
males than for females

Relative Risk Example
Suppose we have 100 men and 100 women,
and 70 men and 10 women have been exposed
Relative risk of exposure (male)=70/100=0.7
Relative risk of exposure(female)=10/100=0.1
The relative risk=0.7/0.1=7
Men 7 times more likely to be exposed than
women


Pattern/Rule Based Contrasts
Overview of ``relational’’ contrast pattern mining
Emerging patterns and mining
Jumping emerging patterns
Computational complexity
Border differential algorithm
• Gene club + border differential
• Incremental mining
Tree based algorithm
Projection based algorithm
ZBDD based algorithm
Bioinformatic application: cancer study on microarray
gene expression data

Overview
Class based association rules (Cai et al 90, Liu et al 98, ...)
Version spaces (Mitchell 77)
Emerging patterns (Dong+Li 99) – many algorithms (later)
Contrast set mining (Bay+Pazzani 99, Webb et al 03)
Odds ratio rules & delta discriminative EP (Li et al 05, Li et
al 07)
MDL based contrast (Siebes, KDD07)
Using statistical measures to evaluate group differences
(Hilderman+Peckman 05, Webb 07)
Spatial contrast patterns (Arunasalam et al 05)
…… see references

Classification/Association Rules
Classification rules -- special association rules
(with just one item – class -- on RHS):
X C (s,c)
• X is a pattern,
• C is a class,
• s is support,
• c is confidence


Version Space (Mitchell)
Version space: the set of all patterns consistent with
given (D+,D-) – patterns separating D+, D-.
The space is delimited by a specific & a general boundary.
Useful for searching the true hypothesis, which lies somewhere
b/w the two boundaries.
Adding +ve examples to D+ makes the specific boundary more
general; adding -ve examples to D- makes the general
boundary more specific.
Common pattern/hypothesis language operators:
conjunction, disjunction
Patterns/hypotheses are crisp; need to be generalized
to deal with percentages; hard to deal with noise in data


STUCCO, MAGNUM OPUS for contrast
pattern mining
STUCCO (Bay+Pazzani 99)
Mining contrast patterns X (called contrast sets) between
k>=2 groups: |suppi(X) – suppj(X)| >= minDiff
Use Chi2 to measure statistical significance of contrast patterns
• significance cut-off thresholds change, based on the level of the
node and the local number of contrast patterns
Max-Miner like search strategy, plus some pruning techniques
MAGNUM OPUS (Webb 01)
An association rule mining method, using Max-Miner like
approach (proposed before, and independently of, Max-Miner)
Can mine contrast patterns (by limiting RHS to a class)


Contrast patterns vs decision tree
based rules
It has been recognized by several authors (e.g.
Bay+Pazzani 99) that
rules generation from decision trees can be good
contrast patterns,
but may miss many good contrast patterns.
Different contrast set mining algorithms have
different thresholds
Some have min support threshold
Some have no min support threshold; low support
patterns may be useful for classification etc


Emerging Patterns
Emerging Patterns (EPs) are contrast patterns between two
classes of data whose support changes significantly between the
two classes. Change significance can be defined by:
big support ratio: similar to RiskRatio; +:
allowing patterns with
supp2(X)/supp1(X) >= minRatio
small overall support
big support difference:
(as defined by Bay+Pazzani 99)
|supp2(X) – supp1(X)| >= minDiff

If supp2(X)/supp1(X) = infinity, then X is a jumping EP.
jumping EP occurs in some members of one class but never
occurs in the other class.
Conjunctive language; extension to disjunctive EP later


A typical EP in the Mushroom dataset
The Mushroom dataset contains two classes: edible
and poisonous.
Each data tuple has several features such as: odor,
ring-number, stalk-surface-bellow-ring, etc.
Consider the pattern
{odor = none,
stalk-surface-below-ring = smooth,
ring-number = one}
Its support increases from 0.2% in the poisonous class to
57.6% in the edible class (a growth rate of 288).


Example EP in microarray data for cancer
Normal Tissues Cancer Tissues

binned
g1 g2 g3 g4 g1 g2 g3 g4
data
L H L H H H L H
L H L L L H H H
H L L H L L L H
L H H L H H H L

Jumping EP: Patterns w/ high support ratio b/w data classes
E.G. {g1=L,g2=H,g3=L}; suppN=50%, suppC=0


Top support minimal jumping EPs
for colon cancer
These EPs have 95%-
-100% support in one
Colon Cancer EPs Colon Normal EPs class but 0% support
{1+ 4- 112+ 113+} 100% {12- 21- 35+ 40+ 137+ 254+} 100% in the other class.
{1+ 4- 113+ 116+} 100% {12- 35+ 40+ 71- 137+ 254+} 100%
Minimal: Each proper
{1+ 4- 113+ 221+} 100% {20- 21- 35+ 137+ 254+} 100%
subset occurs in both
{1+ 4- 113+ 696+} 100% {20- 35+ 71- 137+ 254+} 100%
classes.
{5- 35+ 137+ 177+} 95.5%
{1+ 108- 112+ 113+} 100%
{5- 35+ 137+ 254+} 95.5%
{1+ 108- 113+ 116+} 100% EPs from
{5- 35+ 137+ 419-} 95.5%
{4- 108- 112+ 113+} 100% Mao+Dong 2005
{5- 137+ 177+ 309+} 95.5%
{4- 109+ 113+ 700+} 100% (gene club +
{5- 137+ 254+ 309+} 95.5%
{4- 110+ 112+ 113+} 100% border-diff).
{7- 21- 33+ 35+ 69+} 95.5%
{4- 112+ 113+ 700+} 100% {7- 21- 33+ 69+ 309+} 95.5%
There are ~1000 items
{4- 113+ 117+ 700+} 100% {7- 21- 33+ 69+ 1261+} 95.5%
with supp >= 80%.
{1+ 6+ 8- 700+} 97.5%

Very few 100% support EPs. Colon cancer dataset (Alon et al,
1999 (PNAS)): 40 cancer tissues,
22 normal tissues. 2000 genes

A potential use of minimal jumping EPs
Minimal jumping EPs for normal tissues
Properly expressed gene groups important for normal cell functioning, but
destroyed in all colon cancer tissues
Restore these ?cure colon cancer?

Minimal jumping EPs for cancer tissues
Bad gene groups that occur in some cancer tissues but never occur in normal
tissues
Disrupt these ?cure colon cancer?

? Possible targets for drug design ? Li+Wong 2002 proposed
“gene therapy using EP”
idea: therapy aims to destroy
bad JEP & restore good JEP


Usefulness of Emerging Patterns
EPs are useful
for building highly accurate and robust classifiers, and for improving other types
of classifiers
for discovering powerful distinguishing features between datasets.
Like other patterns composed of conjunctive combination of elements, EPs
are easy for people to understand and use directly.
EPs can also capture patterns about change over time.

Papers using EP techniques in Cancer Cell (cover, 3/02).
Emerging Patterns have been applied in medical applications for
diagnosing acute Lymphoblastic Leukemia.


The landscape of EPs on the support plane,
and challenges for mining

Landscape of EPs Challenges for EP
mining
1
• EP minRatio constraint is
neither monotonic nor anti-
C monotonic (but exceptions
exist for special cases)
• Requires smaller support

Sup D1 (X)
thresholds than those used
for frequent pattern mining

O A Sup D2 (X) B
1


Odds Ratio and Relative Risk
Patterns [Li and Wong PODS06]
May use odds ratio/relative risk to
evaluate compound factors as well
May be no single factor with high relative risk
or odds ratio, but a combination of factors
• Relative risk patterns - Similar to emerging
patterns
• Risk difference patterns - Similar to contrast sets
• Odds ratio patterns


Mining Patterns with High Odds
Ratio or Relative Risk
Space of odds ratio patterns and relative
risk patterns are not convex in general
Can become convex, if stratified into
plateaus, based on support levels


EP Mining Algorithms
Complexity result (Wang et al 05)
Border-differential algorithm (Dong+Li 99)
Gene club + border differential (Mao+Dong 05)
Constraint-based approach (Zhang et al 00)
Tree-based approach (Bailey et al 02,
Fan+Kotagiri 02)
Projection based algorithm (Bailey el al 03)
ZBDD based method (Loekito+Bailey 06).


Complexity result
The complexity of finding emerging
patterns (even those with the highest
frequency) is MAX SNP-hard.
This implies that polynomial time
approximation schemes do not exist for the
problem unless P=NP.


Borders are concise representations of
convex collections of itemsets
< minB={12,13}, maxB={12345,12456}>

123, 1234
12 124, 1235 12345 A collection S is convex:
125, 1245 12456 If for all X,Y,Z (X in S, Y
in S, X subset Z subset
126, 1246 Y) Z in S.
13 134, 1256
135, 1345


Border-Differential Algorithm
<{{}},{1234}> - <{{}},{23,24,34}> Algorithm:
= <{1,234},{1234}> • Use iterations of
{} expansion &
minimization of
1, 2, 3, 4 “products” of
12, 13, 14, 23, 24, 34 differences
123, 124, 134, 234 • Use tree to speed
1234 up minimization

• Find minimal subsets of 1234 that are not subsets of 23, 24, 34.
• {1,234} = min ({1,4} X {1,3} X {1,2})

Good for: Jumping EPs; EPs in “rectangle regions,” …
Iterative expansion & minimization can be
viewed as optimized Berge hypergraph
transversal algorithm

Gene club + Border Differential
Border-differential can handle up to 75 attributes (using
2003 PC)
For microarray gene expression data, there are
thousands of genes.
(Mao+Dong 05) used border-differential after finding
many gene clubs -- one gene club per gene.
A gene club is a set of k genes strongly correlated with
a given gene and the classes.
Some EPs discovered using this method were shown
earlier. Discovered more EPs with near 100% support in
cancer or normal, involving many different genes. Much
better than earlier results.


Tree-based algorithm for JEP mining
Use tree to compress data and patterns.
Tree is similar to FP tree, but it stores two counts per
node (one per class) and uses different item ordering
Nodes with non-zero support for positive class and zero
support for negative class are called base nodes.
For every base node, the path’s itemset is a potential
JEP. Gather negative data containing root item and item
for based nodes on the path. Call border differential.
Item ordering is important. Hybrid (support ratio
ordering first for a percentage of items, frequency
ordering for other items) is best.


Projection based algorithm
Form dataset H containing the differences Let H be:
{p-ni | i=1…k}. a b c d (edge 1)
p is a positive transaction, n1, …, nk are negative b e d (edge 2)
transactions. b c e (edge 3)
Find minimal transverals of hypergraph H. i.e. The c d e (edge 4)
smallest sets intersecting every edge (equivalent to
the smallest subsets of p not contained in any ni).
Item ordering:
Let x1<…<xm be increasing item frequency (in a<b<c<d<e
H) ordering.
For i=1 to m Ha is H with all
let Hxi be H with all items y > xi projected out & items > a (red
all transactions containing xi removed (data
projection). items)
remove non minimal transactions in Hxi. projected out
if Hxi is small, apply border differential and also edge
Otherwise, apply the algorithm on Hxi. with a removed,
so Ha={}.

ZBDD based algorithm
to mine disjunctive emerging patterns

Disjunctive Emerging Patterns: allowing
disjunction as well as conjunction of
simple attribute conditions.
e.g. Precipitation = ( gt-norm OR lt-norm ) AND
Internal discoloration = ( brown OR black )
Generalization of EPs
ZBDD based algorithm uses Zero Suppressed
Binary Decision Diagram for efficiently mining
disjunctive EPs.


Binary Decision Diagrams (BDDs)
Popular in boolean SAT solvers and reliability eng.
Canonical DAG representations of boolean formulae
root
f = (c Λ a) v (d Λ a) 0 c 1
d c
a d
0 a 0 1
dotted (or 0) edge: don’t link a
the nodes (in formulae) 0 1 0 1
Node sharing: identical nodes are shared
Caching principle: past computation results are automatically stored
and can be retrieved
Efficient BDD implementations available, e.g. CUDD (U of Colorado)


ZBDD Representation of Itemsets
Zero-suppressed BDD, ZBDD : A BDD variant for manipulation of item
combinations
E.g. Building a ZBDD for {{a,b,c,e},{a,b,d,e},{b,c,d}}
Ordering : c < d < a < e < b
{{a,b,c,e}} Uz {{a,b,d,e}}= {{a,b,c,e},{a,b,d,e}} Uz {{b,c,d}} = {{a,b,c,e},{a,b,d,e},
{b,c,d}}
c d c c c
d d
a a a d d
a
e U z e = e z U e
=
b b b b b

0 1 0 1 0 1 0 1 0 1

Uz = ZBDD set-union

ZBDD based mining example
Use solid paths in ZBDD(Dn) to generate candidates, and use Bitmap
of Dp to check frequency support in Dp.

ZBDD(Dn) Bitmap
abcdefghi
Dp Dn
a Dp= P1: 1 0 0 0 1 0 1 0 0
P2: 1 0 0 1 0 0 0 0 1
A1 A2 A3 A1 A2 A3 c c P3: 0 1 0 0 0 1 0 1 0
a e g a f g P4: 0 0 1 0 1 0 0 1 0
d d
d
a d i b d h N1: 1 0 0 0 0 1 1 0 0
b e Dn= N2: 0 1 0 1 0 0 0 1 0
f h e
b b f h e N3: 0 1 0 0 0 1 0 1 0
f f N4: 0 0 1 0 1 0 1 0 0
c e h c e g b
h g

1
Ordering: a<c<d<e<b<f<g<h

Contrast pattern based classification
-- history
Contrast pattern based classification: Methods to build or improve
classifiers, using contrast patterns
CBA (Liu et al 98)
CAEP (Dong et al 99)
Instance based method: DeEPs (Li et al 00, 04)
Jumping EP based (Li et al 00), Information based (Zhang et al 00), Bayesian
based (Fan+Kotagiri 03), improving scoring for >=3 classes (Bailey et al 03)
CMAR (Li et al 01)
Top-ranked EP based PCL (Li+Wong 02)
CPAR (Yin+Han 03)
Weighted decision tree (Alhammady+Kotagiri 06)
Rare class classification (Alhammady+Kotagiri 04)
Constructing supplementary training instances (Alhammady+Kotagiri 05)
Noise tolerant classification (Fan+Kotagiri 04)
EP length based 1-class classification of rare cases (Chen+Dong 06)
…
Most follow the aggregating approach of CAEP.


EP-based classifiers: rationale
Consider a typical EP in the Mushroom dataset, {odor = none,
stalk-surface-below-ring = smooth, ring-number = one}; its support
increases from 0.2% from “poisonous” to 57.6% in “edible” (growth
rate = 288).
Strong differentiating power: if a test T contains this EP, we can
predict T as edible with high confidence 99.6% = 57.6/(57.6+0.2)
A single EP is usually sharp in telling the class of a small fraction
(e.g. 3%) of all instances. Need to aggregate the power of many
EPs to make the classification.
EP based classification methods often out perform state of the art
classifiers, including C4.5 and SVM. They are also noise tolerant.


CAEP (Classification by Aggregating Emerging Patterns)
Given a test case T, obtain T’s scores for each class, by
aggregating the discriminating power of EPs contained by T; assign
the class with the maximal score as T’s class.
The discriminating power of EPs are expressed in terms of
supports and growth rates. Prefer large supRatio, large support
The contribution of one EP X (support weighted confidence):
strength(X) = sup(X) * supRatio(X) / (supRatio(X)+1) Compare CMAR:
Chi2 weighted Chi2
Given a test T and a set E(Ci) of EPs for class Ci, the
aggregate score of T for Ci is
score(T, Ci) = Σ strength(X)
(over X of Ci matching T)

For each class, using median (or 85%) aggregated value to
normalize to avoid bias towards class with more EPs

How CAEP works? An example
Class 1 (D1)
Given a test T={a,d,e}, how to classify T?
a c d e
a e
● T contains EPs of class 1 : {a,e} (50%:25%) and
{d,e} (50%:25%), so Score(T, class1) = b c d e
b
0.5*[2/(2+1)] + 0.5*[2/(2+1)] = 0.67
Class 2 (D2)
● T contains EPs of class 2: {a,d} (25%:50%), so
Score(T, class 2) = 0.33; a b

● T will be classified as class 1 since a b c d
Score1>Score2 c e
a b d e

DeEPs (Decision-making by Emerging Patterns)
An instance based (lazy) learning method, like k-NN; but does not
use normal distance measure.
For a test instance T, DeEPs
First project each training instance to contain only items in T
Discover EPs from the projected data
Then use these EPs to select training data that match some discovered
EPs
Finally, use the proportional size of matching data in a class C as T’s
score for C
Advantage: disallow similar EPs to give duplicate votes!


DeEPs : Play-Golf example (data projection)
Test = {sunny, mild, high, true}
Original data Projected data
Outlook Temperature Humidity Windy Class Outlook Temperature HumidityWindy Class
sunny hot high false N sunny high N
sunny hot high true N sunny high true N
rain cool normal true N true N
sunny mild high false N sunny mild high N
rain mild high true N mild high true N
overcast hot high FALSE P high P
rain mild high FALSE P mild high P
rain cool normal FALSE P
overcast cool normal TRUE P
TRUE P
sunny cool normal FALSE P
sunny P
rain mild normal FALSE P
mild P
sunny mild normal TRUE P
sunny mild TRUE P
overcast mild high TRUE P
mild high TRUE P
overcast hot normal FALSE P

Discover EPs and derive scores using the
projected data

PCL (Prediction by Collective Likelihood)
Let X1,…,Xm be the m (e.g. 1000) most general EPs in descending
support order.
Given a test case T, consider the list of all EPs that match T. Divide
this list by EP’s class, and list them in descending support order:
P class: Xi1, …, Xip
N class: Xj1, …, Xjn
Use k (e.g. 15) top ranked matching EPs to get score for T for the P
class (similarly for N):
Score(T,P) = Σt=1k suppP(Xit) / suppP(Xt)

normalizing
factor

Emerging pattern selection factors
There are many EPs, can’t use them all. Should
select and use a good subset.
EP selection considerations include
Keep minimal (shortest, most general) ones
Remove syntactically similar ones
Use support/growth rate improvement (between
superset/subset pairs) to prune
Use instance coverage/overlap to prune
Using only infinite growth rate ones (JEPs)
……


Why EP-based classifiers are good
Use the discriminating power of low support EPs, together with
high support ones
Use multi-feature conditions, not just single-feature conditions
Select from larger pools of discriminative conditions
Compare: Search space of patterns for decision trees is limited by
early greedy choices.
Aggregate/combine discriminating power of a diversified
committee of “experts” (EPs)
Decision is highly explainable


Some other works
CBA (Liu et al 98) uses one rule to make a classification
prediction for a test
CMAR (Li et al 01) uses aggregated (Ch2 weighted)
Chi2 of matching rules
CPAR (Yin+Han 03) uses aggregation by averaging: it
uses the average accuracy of top k rules for each class
matching a test case
…


Aggregating EPs/rules vs bagging
(classifier ensembles)
Bagging/ensembles: a committee of classifiers
vote
Each classifier is fairly accurate for a large
population (e.g. >51% accurate for 2 classes)
Aggregating EPs/rules: matching patterns/rules
vote
Each pattern/rule is accurate on a very small
population, but inaccurate if used as a classifier on
all data; e.g. 99% accurate on 2% of data, but <2%
accurate on all data


Using contrasts for rare class data
[Al Hammady and Ramamohanarao 04,05,06]

Rare class data is important in many
applications
Intrusion detection (1% of samples are
attacks)
Fraud detection (1% of samples are fraud)
Customer click thrus (1% of customers make
a purchase)
…..


Rare Class Datasets
Due to the class imbalance, can
encounter some problems
Few instances in the rare class, difficult to
train a classifier
Few contrasts for the rare class
Poor quality contrasts for the majority class
Need to either increase the instances in
the rare class or generate extra contrasts
for it

Synthesising new contrasts
(new emerging patterns)
Synthesising new emerging patterns by
superposition of high growth rate items
Suppose that attribute A2=`a’ has high growth rate
and that {A1=`x’, A2=`y’} is an emerging pattern.
Then create a new emerging pattern {A1=‘x’, A2=‘a’}
and test its quality.
A simple heuristic, but can give surprisingly
good classification performance


Synthesising new data instances
Can also use previously found contrasts as the basis for
constructing new rare class instances
Combine overlapping contrasts and high growth rate
items
Main idea - intersect & `cross product’ the emerging
patterns & high growth rate (support ratio) items
Find emerging patterns
Cluster emerging patterns into groups that cover all
the attributes
Combine patterns within each group to form
instances

Synthesising new instances
E1{A1=1, A2=X1}, E2{A5=Y1,A6=2,A7=3},
E3{A2=X2,A3=4,A5=Y2} - this is a group
V4 is a high growth item for A4
Combine E1+E2+E3+{A4=V4} to get four synthetic instances.

A1 A2 A3 A4 A5 A6 A7
1 X1 4 V4 Y1 2 3
1 X1 4 V4 Y2 2 3
1 X2 4 V4 Y1 2 3
1 X2 4 V4 Y2 2 3


Measuring instance quality using
emerging patterns
[Al Hammady and Ramamohanarao 07]

Classifiers usually assume that data instances
are related to only a single class (crisp
assignments).
However, real life datasets suffer from noise.
Also, when experts assign an instance to a
class, they first assign scores to each class and
then assign the class with the highest score.
Thus, an instance may in fact be related to
several classes


Measuring instance quality Cont.
For each instance i, assign a weight for its
strength of membership in each class.
Can use emerging patterns to determine
appropriate weights for instances
Weight(i) = aggregation of EPs divided by
mean value for instances in that class
Use these weights in a modified version of
classifier, e.g. a decision tree
Modify information gain calculation to take weights
into account

Using EPs to build Weighted Decision Trees
Instead of crisp class
membership,
let instances have weighted An instance Xi’s membership
class membership, in k classes: (Wi1,…,Wik)
then build weighted decision
trees, where probabilities are
∧ ∧ ∑Wi1 ∧ ∑Wik
computed from the weighted i∈T i∈T
P(T ) = ( p (T ) = 1 k
,..., p (T ) = )
membership. |T | |T |
DeEPs and other EP based ∧ k ∧ ∧
classifiers can be used to assign InfoWDT ( P (T )) = −∑ p j (T ) * log 2 ( p j (T ))
j =1
weights.
m ∧
| Tl |
InfoWDT ( A, T ) = ∑ Info( P(Tl ))
l =1 | T |


Measuring instance quality by
emerging patterns Cont.
More effective than k-NN techniques for
assigning weights
Less sensitive to noise
Not dependent on distance metric
Takes into account all instances, not just
close neighbors


Data cube based contrasts
(Conditional Contrasts)
Gradient (Dong et al 01), cubegrade (Imielinski et al 02
– TR published in 2000):
Mining syntactically similar cube cells, having significantly
different measure values
Syntactically similar: ancestor-descendant or sibling-sibling pair
Can be viewed as “conditional contrasts”: two neighboring
patterns with big difference in performance/measure
Data cubes useful for analyzing multi-dimensional,
multi-level, time-dependent data.
Gradient mining useful for MDML analysis in marketing,
business decisioning, medical/scientific studies


Decision support in data cubes
Used for discovering patterns captured in consolidated historical
data for a company/organization:
rules, anomalies, unusual factor combinations
Focus on modeling & analysis of data for decision makers, not daily
operations.
Data organized around major subjects or factors, such as
customer, product, time, sales.
Cube “contains” huge number of MDML “segment” or “sector”
summaries at different levels of details
Basic OLAP operations: Drill down, roll up, slice and dice, pivot


Data Cubes: Base Table & Hierarchies
Base table stores sales volume (measure), a function of
product, time, & location (dimensions)

Time Hierarchical summarization paths

no
c ati
Industry Region Year

Lo
Category Country Quarter

Product City Month Week

Product
Office Day

*: all (as top of
a base cell each dimension)

Data Cubes: Derived Cells
Time Measures:

t
1Qtr 2Qtr 3Qtr 4Qtr sum sum, count,

uc
TV

d
avg, max,

ro
PC U.S.A

P
VCR min, std, …
sum
Canada
Location

Mexico

(TV,*,Mexico)
sum

Derived cells, different levels of details

Data Cubes: Cell Lattice Compare:
cuboid lattice
(*,*,*)

(a1,*,*) (a2,*,*) (*,b1,*) …

…
(a1,b1,*) (a1,b2,*) (a2,b1,*)

…
(a1,b1,c1) (a1,b1,c2) (a1,b2,c1)


Gradient mining in data cubes
Users want: more powerful (OLAM) support: Find
potentially interesting cells from the billions!
OLAP operations used to help users search in huge space of
cells
Users do: mousing, eye-balling, memoing, decisioning, …
Gradient mining: Find syntactically similar cells with
significantly different measure values
(teen clothing,California,2006), total-profit=100K
vs (teen clothing,Pensylvania,2006), total profit = 10K

A specific OLAM task


LiveSet-Driven Algorithm for
constrained gradient mining
Set-oriented processing; traverse the cube while carrying the live
set of cells having potential to match descendants of the current
cell as gradient cells
A gradient compares two cells; one is the probe cell, & the other is a
gradient cell. Probe cells are ancestor or sibling cells
Traverse the cell space in a coarse-to-fine manner, looking for
matchable gradient cells with potential to satisfy gradient constraint
Dynamically prune the live set during traversal
Compare: Naïve method checks each possible cell pair


Pruning probe cells using dimension
matching analysis
Defn: Probe cell p=(a1,…,an) is matchable with
gradient cell g=(b1, …, bn) iff
No solid-mismatch, or
Only one solid-mismatch but no *-mismatch

A solid-mismatch: if aj≠bj + none of aj or bj is *
A *-mismatch: if aj=* and bj≠*

p=(00, Tor, *, *) : 1 solid
g=(00, Chi, *,PC) : 1 *
Thm: cell p is matchable with cell g iff p may make a probe-gradient pair with some
descendant of g (using only dimension value info)


Sequence based contrasts
We want to compare sequence datasets:
bioinformatics (DNA, protein), web log, job/workflow history,
books/documents
e.g. compare protein families; compare bible books/versions
Sequence data are very different from relational data
order/position matters
unbounded number of “flexible dimensions”
Sequence contrasts in terms of 2 types of comparison:
Dataset based: Positive vs Negative
• Distinguishing sequence patterns with gap constraints (Ji et al 05, 07)
• Emerging substrings (Chan et al 03)
Site based: Near marker vs away from marker
• Motifs
• May also involve data classes Roughly: A site is a position
in a sequence where a
special marker/pattern occurs

Example sequence contrasts
When comparing the two protein families zf-C2H2 and zf-
CCHC, we discovered a protein MDS CLHH appearing as a
subsequence in 141 of196 protein sequences of zf-C2H2
but never appearing in the 208 sequences in zf-CCHC.

When comparing the first and last books from the Bible,
we found the subsequences (with gaps) “having horns”,
“face worship”, “stones price” and “ornaments price”
appear multiple times in sentences in the Book of
Revelation, but never in the Book of Genesis.


Sequence and sequence pattern
occurrence
A sequence S = e1e2e3…en is an ordered list of items over a given
alphabet.
E.G. “AGCA” is a DNA sequence over the alphabet {A, C, G, T}.
“AC” is a subsequence of “AGCA” but not a substring;
“GCA” is a substring

Given sequence S and a subsequence pattern S’, an occurrence
of S’ in S consists of the positions of the items from S’ in S.
EG: consider S = “ACACBCB”
<1,5>, <1,7>, <3,5>, <3,7> are occurrences of “AB”
<1,2,5>, <1,2,7>, <1,4,5>, … are occurrences of “ACB”


Maximum-gap constraint satisfaction
A (maximum) gap constraint: specified by a positive integer g.
Given S & an occurrence os = <i1, … im>, if ik+1 – ik <= g + 1
for all 1 <= k <m, then os fulfills the g-gap constraint.
If a subsequence S’ has one occurrence fulfilling a gap constraint,
then S’ satisfies the gap constraint.
The <3,5> occurrence of “AB” in S = “ACACBCB”, satisfies the
maximum gap constraint g=1.
The <3,4,5> occurrence of “ACB” in S = “ACACBCB”satisfies the
maximum gap constraint g=1.
The <1,2,5>, <1,4,5>, <3,4,5> occurrences of “ACB” in S =
“ACACBCB”satisfy the maximum gap constraint g=2.

One sequence contributes to at most one to count.


g-MDS Mining Problem
Given two sets pos & neg of sequences, two support
β
thresholds minp & minn, & a maximum gap g, a pattern p
is a Minimal Distinguishing Subsequence with g-gap
constraint (g-MDS), if these conditions are met:
1. Frequency condition: supppos(p,g) >= minp;
2. Infrequency condition: suppneg(p,g) <= minn; β
3. Minimality condition: There is no subsequence of p satisfying 1 & 2.

Given pos, neg, minp, minn and g, the g-MDS mining
problem is to find all the g-MDSs.
β


Example g-MDS
Given minp=1/3, minn=0, g=1,
pos = {CBAB, AACCB, BBAAC},
neg = {BCAB,ABACB}
1-MDS are: BB, CC, BAA, CBA
“ACC” is frequent in pos & non-occurring in neg, but it is not
minimal (its subsequence “CC” meets the first two conditions).


g-MDS mining : Challenges
The min support thresholds in mining distinguishing
patterns need to be lower than those used for mining
frequent patterns.
Min supports offer very weak pruning power on the large
search space.
Maximum gap constraint is neither monotone nor
anti-monotone.
Gap checking requires clever handling.


ConSGapMiner
The ConSGapMiner algorithm works in three steps:
1. Candidate Generation:
Candidates are generated without duplication. Efficient
pruning strategies are employed.

2. Support Calculation and Gap Checking:
For each generated candidate c, supppos(c,g) and
suppneg(c,g) are calculated using bitset operations.

3. Minimization:
Remove all the non-minimal patterns (using pattern trees).


ConSGapMiner : Candidate Generation
{}
ID Sequence Class
1 CBAB pos
A (3, 2) B (3, 2) C (3, 2)
2 AACCB pos
3 BBAAC pos AA (2, 1) … … …
4 BCAB neg
5 ABACB neg AAA (0, 0) AAB (0, 1) AAC (2, 1)
• DFS tree
• Two counts per node/pattern AACA (0, 0) AACB (1, 1) AACC (1, 0)
• Don’t extend pos-infrequent patterns
• Avoid duplicates & certain non-minimal g- AACBA (0, 0) AACBB (0, 0) AACBC (0, 0)
MDS (e.g. don’t extend g-MDS)

Use Bitset Operation for Gap Checking
Storing projected suffixes and Projections with prefix A :
performing scans is expensive.
ACTGTATTACCAGTATCG
e.g. Given a sequence ATTACCAGTATCG
ACTGTATTACCAGTATCG ACCAGTATCG
to check whether AG is a AGTATCG
subsequence for g=1:
ATCG

We encode the occurrences’
Projections with AG obtained
ending positions into a bitset
and use a series of bitwise
from the above:
operations to generate a new
candidate sequence’s bitset.

AGTATCG


ConSGapMiner: Support & Gap Checking (1)
Initial Bitset Array Construction: For each item x,
construct an array of bitsets to describe where x occurs
in each sequence from pos and neg.
Dataset Initial Bitset Array
ID Sequence Class single-item A
1 CBAB pos
0010
2 AACCB pos
11000
3 BBAAC pos
00110
4 BCAB neg
0010
5 ABACB neg
10100


Two steps: (1) g+1 right shifts; (2) OR them
EG: generate mask bitset for X =“A” in sequence 5 (with max gap g = 1):
ID Sequence Class
1 CBAB pos 1 0 1 0 0 > > 0 1 0 1 0
2 AACCB pos
0 1 0 1 0 > > 0 0 1 0 1
3 BBAAC pos
OR
4 BCAB neg
5 ABACB neg Mask bitset for X : 0 1 1 1 1

Mask bitset: all the legal positions in the sequence at most (g+1)-positions
away from tail of an occurrence of the (maximum prefix of the) pattern.


EG: Generate bitset array (ba) for X’ = “BA” from X = ‘B’(g = 1)
1. Get ba for X=‘B’
ba(X): mask(X’):
2. Shift ba(X) to get mask for 0101 0011 Number
X’ = ‘BA’ 2 shifts
00001 00000 of arrays
plus OR
3. AND ba(‘A’) and mask(X’) 11000 01110 with
to get ba(X’) 1001 0110 some 1 =
ID Sequence Class 01001 00110 count
1 CBAB pos mask(X’): ba(‘A’): ba(X’):
2 AACCB pos 0011 0010 0010
3 BBAAC pos 00000 11000
& 00000
4 BCAB neg 01110 00110 00110
5 neg 0110 0010
ABACB 0010
00110 10100 00100

Execution time performance on protein
families
Pos(#) Neg(#) Avg. Len. (Pos, Neg) Pos(#) Neg(#) Avg. Len. (Pos, Neg)
DUF1694 (16) DUF1695 (5) (123, 186) TatC (74) TatD_DNase(119) (205, 262)

1000 10000

100
1000
10

1 100

running time (sec)
running time (sec)
6.25% 12.50% 18.75% 25% 31.25% 5.40% 13.50% 16.20% 18.90% 21.60% 24.30%
minimal support minimal support

runtime vs support, for g = 5 runtime vs support, for g = 5

1000.0 10000
100.0 1000
10.0 100
1.0 10
0.1 1 3 5 7 9 1
running time (sec)

running time (sec)
0.0 3 4 5 6 7
maximal gap maximal gap

runtime vs g, for α = 0.3125(5) runtime vs g, for α = 0.27(20)

α

Pattern Length Distribution
-- Protein Families

The length and frequency distribution of patterns: TaC vs
TatD_DNase, g = 5, α =13.5%.
1000000 1000000

10000 10000

100

#5-MDS
100

#5-MDS
1 1

3 4 5 6 7 8 9 10 11 1~10 11~20 21~30 31~40 41~50 >50

length of patterns frequency count

Length distribution Frequency distribution


Bible Books Experiment

New Testament (Matthew, Mark, Luke and John) vs
40 Old Testament (Genesis, Exodus, Leviticus and Numbers):
30
20
10 #Pos #Neg Alphabet Avg. Len. Max. Len.
0

running time (sec)
0.13% 0.27% 0.40% 0.53% 0.66%
3768 4893 3344 7 25
minimal support

runtime vs support, for g = 6.
Some interesting terms found from the Bible
40
35
books (New Testament vs Old Testament):
30
25 Substrings (count) Subsequences (count)
20

running time (sec)
0 2 4 6 8
eternal life (24) seated hand (10)
maximal gap

good news (23) answer truly (10)
runtime vs g, for α = 0.0013.
Forgiveness in (22) Question saying (13)
Chief priests (53) Truly kingdom (12)


Extensions
Allowing min gap constraint
Allowing max window length constraint
Considering different minimization strategies:
Subsequence-based minimization (described on
previous slides)
Coverage (matching tidset containment) +
subsequence based minimization
Prefix based minimization


Motif mining
Find sequence patterns frequent around a site marker,
but infrequent elsewhere
Can also consider two classes:
Find patterns frequent around site marker in +ve class, but in
frequent at other positions, and infrequent around site marker in
–ve class
Often, biological studies use background probabilities instead of
a real -ve dataset
Popular concept/tool in biological studies


Contrasts for Graph Data
Can capture structural differences
Subgraphs appearing in one class but not in
the other class
• Chemical compound analysis
• Social network comparison


Contrasts for graph data Cont.
Standard frequent subgraph mining
Given a graph database, find connected
subgraphs appearing frequently
Contrast subgraphs particularly focus on
discrimination and minimality


Minimal contrast subgraphs [Ting and Bailey 06]
A contrast graph is a subgraph appearing
in one class of graphs and never in
another class of graphs
Minimal if none of its subgraphs are contrasts
May be disconnected
• Allows succinct description of differences
• But requires larger search space
Will focus on one versus one case

Contrast subgraph example
v0(a) Negative v0(a)
Positive
e0(a) e1(a)
e0(a) e1(a)
v1(a) v2(a)
e2(a)
v1(a) v2(a)
e2(a) e3(a)
e3(a) e4(a)
v3(a) e (a) v4(a)
v3(c) 4

Graph A Graph B

v0(a) v0(a)
Contrast Contrast Contrast
e0(a) e1(a) e0(a)
e2(a) v3(c) v3(c)
v1(a) v2(a) v1(a)
Graph C Graph D Graph E

Minimal contrast subgraphs
Minimal contrast graphs are of two types
Those with only vertices (a vertex set)
Those without isolated vertices (edge sets)
Can prove that for 1-1 case, the minimal
contrast subgraphs are the union of

Min. Con. Vertex Sets + Min. Con. Edge Sets

Mining contrast subgraphs
Main idea
Find the maximal common edge sets
• These may be disconnected
Apply a minimal hypergraph transversal
operation to derive the minimal contrast edge
sets from the maximal common edge sets
Must compute minimal contrast vertex sets
separately and then minimal union with the
minimal contrast edge sets


Contrast graph mining workflow
Maximal Common
Negative Edge Sets 1
Graph
Gn1 (Maximal Common
Vertex Sets 1)

Maximal Complements of Minimal
Maximal Common Common Maximal Common Minimal Contrast
Positive Negative Edge Sets 2 Edge Sets Complement Edge Sets Transversals Edge Sets
Graph Graph
Gp Gn2 (Maximal Common (Maximal (Complements of (Minimal
Vertex Sets 2) Common Maximal Common Vertex

Vertex Sets) Vertex Sets) Sets)

Maximal Common
Negative Edge Sets 3
Graph
Gn3 (Maximal Common
Vertex Sets 1)


Using discriminative graphs for
containment search and indexing
[Chen et al 07]

Given a graph database and a query q. Find all graphs
in the database contained in q.
query graph q

models contained by q
model graph database D

Applications
Querying image databases represented as attributed relational
graphs. Efficiently find all objects from the database contained
in a given scene (query).


Discriminative graphs for indexing
Cont.

Main idea:
Given a query graph q and a database graph
g
• If a feature f is not contained in q and f is
contained in g, then g is not contained in q
Also exploit similarity between graphs.
If f is a common substructure between g1
and g2, then if f is not contained in the query,
both g1 and g2 are not contained in the
query

Graph Containment Example [From
Chen et al 07]

ga gb gc

(ga) (gb) (gc) f1 1 1 1
A Sample Database
f2 1 1 0

f3 1 1 0
(f1) (f2) (f3) (f4) f4 1 0 0
Features


Discriminative graphs for indexing
Aim to select the ``contrast features’’ that have
the most pruning power (save most
isomorphism tests)
These are features that are contained by many
graphs in the database, but are unlikely to be
contained by a query graph.
Generate lots of candidates using a frequent
subgraph mining and then filter output graphs
for discriminative power


Generating the Index
After the contrast subgraphs have been
found, select a subset of them
Use a set cover heuristic to select a set that
``covers’’ all the graphs in the database, in
the context of a given query q
For multiple queries, use a maximum
coverage with cost approach


Contrasts for trees
Special case of graphs
Lower complexity
Lots of activity in the document/XML area, for
change detection.
Notions such as edit distance more typical
for this context


Contrasts of models
Models can be clusterings, decision trees, …
Why is contrasting useful here ?
Contrast/compare a user generated model against a
known reference model, to evaluate accuracy/degree
of difference.
May wish to compare degree of difference between
one algorithm using varying parameters
Eliminate redundancy among models by choosing
dissimilar representatives


Contrasts of models Cont.
Isn’t this just a dissimilarity measure ?
Like Euclidean distance ?
Similar, but operating on more complex
objects, not just vectors
Difficulties are
For rule based classifiers, can’t just report on
number of different rules


Clustering comparison
Popular clustering comparison measures
Rand index and Jaccard index
• Measure the proportion of point pairs on which the
two clusterings agree
Mutual information
• How much information one clustering gives about
the other
Clustering error
• Classification error metric

Clustering Comparison Measures
Nearly all techniques use a ‘Confusion Matrix’
of two clusterings. Example : Let C = {c1, c2, c3)
and C’ = {c’1, c’2, c’3}

m c1 c2 c3
c’1 5 14 1 mij = | ci ∩ c’j|
c’2 10 2 8
c’3 8 7 5


Pair counting
Considers the number of points on which two
clusterings agree or disagree. Each pair falls into one
of four categories
N11 – number of pairs of points which are
in the same cluster in both C and C’
not in the same cluster in both C and C’
in the same cluster in C but not in C’
in the same cluster in C’ but not in C
N - total number of pairs of points


Pair Counting
Two popular indexes - Rand and Jaccard

N11 + N00
Rand(C,C’) =
N

N11
Jaccard(C,C’) = N11 + N01 + N10


Clustering Error Metric
(Classification Error Metric)
An injective mapping of C={1,…,K} into
C’={1…,K’}. Need to find maximum
intersection for all possible mappings.
m c1 c2 c3 Best match is
{c2, c’1}, {c1, c’2},
c’1 5 14 1
{c3, c’3}}
c’2 10 2 8
Clustering error=
c’3 8 7 5 (14+10+5)/60=0.483


Clustering Comparison Difficulties

Which most similar to clustering (a)?
Rand(a,b)=Rand(a,c)
Reference Jaccard(a,b)=Jaccard(a,c) !

(a) (b) (c)


Comparing datasets via induced
models
Given two datasets, we may compare their
difference, by considering the difference or
deviation between the models that can be
induced from them
Models here can refer to decision trees,
frequent itemsets, emerging patterns, etc
May also compare an old model to a new
dataset
How much does it misrepresent ?

The FOCUS Framework [Ganti et al 02]
Develops a single measure for quantifying the
difference between the interesting
characteristics in each dataset.
Key Idea: ``A model has a structural component
that identifies interesting regions of the attribute
space … each such region is summarized by
one (or several) measure(s)’’
Difference between two classifiers is measured
by amount of work needed to change them into
some common specialization

Focus Framework Cont.
For comparing two models, divide the
models each into regions and then
compare the regions individually
For a decision tree, compare leaf nodes of
each model
Aggregate the pairwise differences between
each of the regions


Decision tree example [Taken from Ganti et 02]
(class1,class2) (class1’,class2’) (class1-class1’) [0.0-0.0]

(0.1,0.0)
(0.18,0.1) [0.1-0.14]
100K 100K
[0.0-0.04]
(0.05,0.55) 80K (0.1,0.52) 80K
[0.05-0.1]
(0.0,0.3)
(0.0,0.1) [0.0-0.0]

Salary
Salary

Salary
Age 30 Age 50 Age 30 50 [0.0-0.0]

T1:D1 T2:D2 T3: GCR of T1 and T2
(just for class1)
Difference(D1,D2)=|0.0-0.0|+|0.0-0.04|+|0.1-0.14|+|0.0-0.0|+|0.0-0.0|+|0.05-0.1|
=0.13


Correspondence Tracing of
Changes [Wang et al 03]

Correspondence tracing aims to make
change between the two models
understandable by explicitly describing
changes and then ranking them


Correspondence Tracing Example
[Taken from Wang et al 03]

Consider old and new rule based classifiers
Old ID’s of instances classified
O1: If A4=1 then C3 [0,2,7,9,13,15,17]
O2: If A3=1 and A4=2 then C2 [1,4,6,10,12,16]
O3: If A3=2 and A4=2 then C1 [3,5,8,11,14]
New
N1: If A3=1 and A4=1 then C3 [0,9,15]
N2: If A3=1 and A4=2 then C2 [1,4,6,10,12,16]
N3: If A3=2 and A4=1 then C2 [2,7,13,17]
N4: If A3=2 and A4=2 then C1 [3,5,8,11,14]


Correspondence Example cont.
Rules N1 and N3 classify the examples that
were classified by rule O1. So the changes for
the sub population covered by O1 can be
described as
O1,N1 and O1,N3

Changes O2,N2 and O3,N4 are trivial
because the old and new rules are identical.


Rule Accuracy Increase.
The quantitative change Q of O,N is
the estimated accuracy increase (+ or -)
due to the change from O to N.
Changes are ranked according to
quantitative change Q and then presented
to the user


Common themes for contrast
mining
Different representations
Minimality is the most common
Support/ratio constraints quite popular,
though not necessarily the best
Conjunctions most popular for relational case
Large number of contrast patterns are
output


Recommendations to Practitioners
Some important points are
Contrast patterns can capture distinguishing
patterns between classes
Contrast patterns can be used to build high
quality classifiers
Contrast patterns can capture useful patterns
for detecting/treating diseases, or other
events/conditions


Open Problems in Contrast Data
Mining
How to meaningfully assess quality of contrasts, especially for non-
relational data.
How to explain the semantics of contrasts
Mining of contrasts using user specified domain knowledge
Highly expressive contrasts (first order ..)
Develop new ways to build contrast based classifiers and finding
the highest impact contrasts
Rare class classification and contrasts still an unsettled issue
Discovery of contrasts in massive datasets.
Efficiently mine contrasts when there are thousands of
attributes, such as in medical domains
Efficient mining of top-k contrast patterns
Are there meaningful approximations (e.g. sampling) ?


Summary
We have given a wide survey of contrast
mining. It should now be clearer
Why contrast data mining is important and
when it can be used
How it can be used for very powerful
classifiers
What algorithms can be used for contrast
data mining


Acknowledgements
We are grateful to the following people for their
helpful comments or materials for this tutorial
Eric Bae
Jiawei Han
Xiaonan Ji
Rao Kotagiri
Jinyan Li
Elsa Loekito
Katherine Ramsay
Limsoon Wong


Bibliography
This bibliography contains three sections:
Mining of Emerging Patterns, Change Patterns,
Contrast/Difference Patterns
Emerging/Contrast Pattern Based Classification
Other Applications of Emerging Patterns
An up to date version of this bibliography is available at

http://www.cs.wright.edu/~gdong/EPC.html

Please let us know of any extra references to include !

Bibliography (Mining of Emerging Patterns, Change Patterns,
Contrast/Difference Patterns)

Arunasalam, Bavani and Chawla, Sanjay and Sun, Pei. Striking Two Birds with One Stone: Simultaneous Mining
of Positive and Negative Spatial Patterns. In Proceedings of the Fifth SIAM International Conference on Data
Mining, April 21-23, pp, Newport Beach, CA, USA, SIAM 2005
Bavani Arunasalam, Sanjay Chawla: CCCS: a top-down associative classifier for imbalanced class distribution.
KDD 2006: 517-522
Eric Bae, James Bailey, Guozhu Dong: Clustering Similarity Comparison Using Density Profiles. Australian
Conference on Artificial Intelligence 2006: 342-351
James Bailey, Thomas Manoukian, Kotagiri Ramamohanarao: Fast Algorithms for Mining Emerging Patterns.
PKDD 2002: 39-50.
J. Bailey and T. Manoukian and K. Ramamohanarao: A Fast Algorithm for Computing Hypergraph Transversals
and its Application in Mining Emerging Patterns. Proceedings of the 3rd IEEE International Conference on Data
Mining (ICDM). Pages 485-488. Florida, USA, November 2003.
Stephen D. Bay, Michael J. Pazzani: Detecting Change in Categorical Data: Mining Contrast Sets. KDD 1999:
302-306.
Stephen D. Bay, Michael J. Pazzani: Detecting Group Differences: Mining Contrast Sets. Data Min. Knowl.
Discov. 5(3): 213-246 (2001)
Cristian Bucila, Johannes Gehrke, Daniel Kifer, Walker M. White: DualMiner: A Dual-Pruning Algorithm for
Itemsets with Constraints. Data Min. Knowl. Discov. 7(3): 241-272 (2003)
Yandong Cai, Nick Cercone, Jiawei Han: An Attribute-Oriented Approach for Learning Classification Rules from
Relational Databases. ICDE 1990: 281-288
Sarah Chan, Ben Kao, Chi Lap Yip, Michael Tang: Mining Emerging Substrings. DASFAA 2003.
Yixin Chen, Guozhu Dong, Jiawei Han, Jian Pei, Benjamin W. Wah, Jianyong Wang: Online Analytical
Processing Stream Data: Is It Feasible? DMKD 2002
Chen Chen, Xifeng Yan, Philip S. Yu, Jiawei Han, Dong-Qing Zhang, Xiaohui Gu: Towards Graph Containment
Search and Indexing. VLDB 2007: 926-937



Graham Cormode, S. Muthukrishnan: What's new: finding significant differences in network data streams.
IEEE/ACM Trans. Netw. 13(6): 1219-1232 (2005)
Luc De Raedt, Albrecht Zimmermann: Constraint-Based Pattern Set Mining. SDM 2007
Luc De Raedt: Towards Query Evaluation in Inductive Databases Using Version Spaces. Database Support for
Data Mining Applications 2004: 117-134
Luc De Raedt, Stefan Kramer: The Levelwise Version Space Algorithm and its Application to Molecular
Fragment Finding. IJCAI 2001: 853-862
Guozhu Dong, Jinyan Li: Efficient Mining of Emerging Patterns: Discovering Trends and Differences. KDD 1999:
43-52.
Guozhu Dong, Jinyan Li: Mining border descriptions of emerging patterns from dataset pairs. Knowl. Inf. Syst.
8(2): 178-202 (2005).
Dong, G. and Han, J. and Lakshmanan, L.V.S. and Pei, J. and Wang, H. and Yu, P.S. Online Mining of Changes
from Data Streams: Research Problems and Preliminary Results, Proceedings of the 2003 ACM SIGMOD
Workshop on Management and Processing of Data Streams, 2003
Guozhu Dong, Jiawei Han, Joyce M. W. Lam, Jian Pei, Ke Wang, Wei Zou: Mining Constrained Gradients in
Large Databases. IEEE Trans. Knowl. Data Eng. 16(8): 922-938 (2004).
Johannes Fischer, Volker Heun, Stefan Kramer: Optimal String Mining Under Frequency Constraints. PKDD
2006: 139-150
Venkatesh Ganti, Johannes Gehrke, Raghu Ramakrishnan: A Framework for Measuring Changes in Data
Characteristics. PODS 1999: 126-137
Venkatesh Ganti, Johannes Gehrke, Raghu Ramakrishnan, Wei-Yin Loh: A Framework for Measuring
Differences in Data Characteristics. J. Comput. Syst. Sci. 64(3): 542-578 (2002)
Garriga, G.C. and Kralj, P. and Lavrac, N. Closed Sets for Labeled Data?, PKDD, 2006
Hilderman, R.J. and Peckham, T. A Statistically Sound Alternative Approach to Mining Contrast Sets,
Proceedings of the 4th Australasian Data Mining Conference, 2005 (pp157-172)



Hui-jing Huang, Yongsong Qin, Xiaofeng Zhu, Jilian Zhang, and Shichao Zhang. Difference
Detection Between Two Contrast Sets. Proceedings of the 8th International Conference on Data
Warehousing and Knowledge Discovery (DaWak), 2006.
Imberman, S.P. and Tansel, A.U. and Pacuit, E. An Efficient Method For Finding Emerging Frequent Itemsets,
3rd International Workshop on Mining Temporal and Sequential Data, pp112--121, 2004
Tomasz Imielinski, Leonid Khachiyan, Amin Abdulghani: Cubegrades: Generalizing Association Rules. Data Min.
Knowl. Discov. 6(3): 219-257 (2002)
Inakoshi, H. and Ando, T. and Sato, A. and Okamoto, S. Discovery of emerging patterns from nearest neighbors,
International Conference on Machine Learning and Cybernetics, 2002.
Xiaonan Ji, James Bailey, Guozhu Dong: Mining Minimal Distinguishing Subsequence Patterns with Gap
Constraints. ICDM 2005: 194-201.
Xiaonan Ji, James Bailey, Guozhu Dong: Mining Minimal Distinguishing Subsequence Patterns with Gap
Constraints. Knowl. Inf. Syst. 11(3): 259--286 (2007).
Daniel Kifer, Shai Ben-David, Johannes Gehrke: Detecting Change in Data Streams. VLDB 2004: 180-191
P Kralj, N Lavrac, D Gamberger, A Krstacic. Contrast Set Mining for Distinguishing Between Similar Diseases.
LNCS Volume 4594, 2007.
Sau Dan Lee, Luc De Raedt: An Efficient Algorithm for Mining String Databases Under Constraints. KDID 2004:
108-129
Haiquan Li, Jinyan Li, Limsoon Wong, Mengling Feng, Yap-Peng Tan: Relative risk and odds ratio: a data mining
perspective. PODS 2005: 368-377
Jinyan Li, Guimei Liu and Limsoon Wong. Mining Statistically Important Equivalence Classes and delta-
Discriminative Emerging Patterns. KDD 2007.
Jinyan Li, Thomas Manoukian, Guozhu Dong, Kotagiri Ramamohanarao: Incremental Maintenance on the
Border of the Space of Emerging Patterns. Data Min. Knowl. Discov. 9(1): 89-116 (2004).



Jinyan Li and Qiang Yang. Strong Compound-Risk Factors: Efficient Discovery through Emerging Patterns and
Contrast Sets. IEEE Transactions on Information Technology in Biomedicine. To appear.
Lin, J. and Keogh, E. Group SAX: Extending the Notion of Contrast Sets to Time Series and Multimedia Data.
Proceedings of the 10th european conference on principles and practice of knowledge discovery in databases.
Berlin, Germany, September, 2006.
Bing Liu, Ke Wang, Lai-Fun Mun, Xin-Zhi Qi: Using Decision Tree Induction for Discovering Holes in Data.
PRICAI 1998: 182-193
Bing Liu, Liang-Ping Ku, Wynne Hsu: Discovering Interesting Holes in Data. IJCAI (2) 1997: 930-935
Bing Liu, Wynne Hsu, Yiming Ma: Discovering the set of fundamental rule changes. KDD 2001: 335-340.
Elsa Loekito, James Bailey: Fast Mining of High Dimensional Expressive Contrast Patterns Using Zero-
suppressed Binary Decision Diagrams. KDD 2006: 307-316.
Yu Meng, Margaret H. Dunham: Efficient Mining of Emerging Events in a Dynamic Spatiotemporal Environment.
PAKDD 2006: 750-754
Tom M. Mitchell: Version Spaces: A Candidate Elimination Approach to Rule Learning. IJCAI 1977: 305-310
Amit Satsangi, Osmar R. Zaiane, Contrasting the Contrast Sets: An Alternative Approach, Eleventh International
Database Engineering and Applications Symposium (IDEAS 2007), Banff, Canada, September 6-8, 2007
Michele Sebag: Delaying the Choice of Bias: A Disjunctive Version Space Approach. ICML 1996: 444-452
Michele Sebag: Using Constraints to Building Version Spaces. ECML 1994: 257-271
Arnaud Soulet, Bruno Crémilleux, François Rioult: Condensed Representation of EPs and Patterns Quantified by
Frequency-Based Measures. KDID 2004: 173-190
Pawel Terlecki, Krzysztof Walczak: On the relation between rough set reducts and jumping emerging patterns.
Inf. Sci. 177(1): 74-83 (2007).



Roger Ming Hieng Ting, James Bailey: Mining Minimal Contrast Subgraph Patterns. SDM 2006.
V. S. Tseng, C. J. Chu, and Tyne Liang, An Efficient Method for Mining Temporal Emerging Itemsets From Data
Streams, International Computer Symposium, Workshop on Software Engineering, Databases and Knowledge
Discovery, 2006
J. Vreeken, M. van Leeuwen, A. Siebes: Characterising the Difference. KDD 2007.
Haixun Wang, Wei Fan, Philip S. Yu, Jiawei Han: Mining concept-drifting data streams using ensemble
classifiers. KDD 2003: 226-235
Peng Wang, Haixun Wang, Xiaochen Wu, Wei Wang, Baile Shi: On Reducing Classifier Granularity in Mining
Concept-Drifting Data Streams. ICDM 2005: 474-481
Lusheng Wang, Hao Zhao, Guozhu Dong, Jianping Li: On the complexity of finding emerging patterns. Theor.
Comput. Sci. 335(1): 15-27 (2005).
Ke Wang, Senqiang Zhou, Ada Wai-Chee Fu, Jeffrey Xu Yu: Mining Changes of Classification by
Correspondence Tracing. SDM 2003.
Geoffrey I. Webb: Discovering Significant Patterns. Machine Learning 68(1): 1-33 (2007)
Geoffrey I. Webb, Songmao Zhang: K-Optimal Rule Discovery. Data Min. Knowl. Discov. 10(1): 39-79 (2005)
Geoffrey I. Webb, Shane M. Butler, Douglas A. Newlands: On detecting differences between groups. KDD 2003:
256-265.
Xiuzhen Zhang, Guozhu Dong, Kotagiri Ramamohanarao: Exploring constraints to efficiently mine emerging
patterns from large high-dimensional datasets. KDD 2000: 310-314.
Lizhuang Zhao, Mohammed J. Zaki, Naren Ramakrishnan: BLOSOM: a framework for mining arbitrary boolean
expressions. KDD 2006: 827-832


Bibliography (Emerging/Contrast Pattern Based
Classification)
Hamad Alhammady, Kotagiri Ramamohanarao: The Application of Emerging Patterns for Improving the Quality
of Rare-Class Classification. PAKDD 2004: 207-211
Hamad Alhammady, Kotagiri Ramamohanarao: Using Emerging Patterns and Decision Trees in Rare-Class
Classification. ICDM 2004: 315-318
Hamad Alhammady, Kotagiri Ramamohanarao: Expanding the Training Data Space Using Emerging Patterns
and Genetic Methods. SDM 2005
Hamad Alhammady, Kotagiri Ramamohanarao: Using Emerging Patterns to Construct Weighted Decision Trees.
IEEE Trans. Knowl. Data Eng.
18(7): 865-876 (2006).
Hamad Alhammady, Kotagiri Ramamohanarao: Mining Emerging Patterns and Classification in Data Streams.
Web Intelligence 2005: 272-275
James Bailey, Thomas Manoukian, Kotagiri Ramamohanarao: Classification Using Constrained Emerging
Patterns. WAIM 2003: 226-237
Guozhu Dong, Xiuzhen Zhang, Limsoon Wong, Jinyan Li: CAEP: Classification by Aggregating Emerging
Patterns. Discovery Science
1999: 30-42.
Hongjian Fan, Kotagiri Ramamohanarao: An Efficient Single-Scan Algorithm for Mining Essential Jumping
Emerging Patterns for Classification. PAKDD 2002: 456-462
Hongjian Fan, Kotagiri Ramamohanarao: Efficiently Mining Interesting Emerging Patterns. WAIM 2003: 189-201
Hongjian Fan, Kotagiri Ramamohanarao: Noise Tolerant Classification by Chi Emerging Patterns. PAKDD 2004:
201-206
Hongjian Fan, Ming Fan, Kotagiri Ramamohanarao, Mengxu Liu: Further Improving Emerging Pattern Based
Classifiers Via Bagging. PAKDD 2006: 91-96
Hongjian Fan, Kotagiri Ramamohanarao: A weighting scheme based on emerging patterns for weighted support
vector machines. GrC 2005: 435-440


Classification)
Hongjian Fan, Kotagiri Ramamohanarao: Fast Discovery and the Generalization of Strong Jumping Emerging
Patterns for Building Compact and Accurate Classifiers. IEEE Trans. Knowl. Data Eng. 18(6): 721-737 (2006)
Jinyan Li, Guozhu Dong, Kotagiri Ramamohanarao: Instance-Based Classification by Emerging Patterns. PKDD
2000: 191-200
Jinyan Li, Guozhu Dong, Kotagiri Ramamohanarao: Making Use of the Most Expressive Jumping Emerging
Patterns for Classification. PAKDD 2000: 220-232
Jinyan Li, Guozhu Dong, Kotagiri Ramamohanarao: Making Use of the Most Expressive Jumping Emerging
Patterns for Classification. Knowl. Inf. Syst. 3(2): 131-145 (2001)
Jinyan Li, Kotagiri Ramamohanarao, Guozhu Dong: Emerging Patterns and Classification. ASIAN 2000: 15-32
Jinyan Li, Guozhu Dong, Kotagiri Ramamohanarao, Limsoon Wong: DeEPs: A New Instance-Based Lazy
Discovery and Classification System. Machine Learning 54(2): 99-124 (2004).
Wenmin Li, Jiawei Han, Jian Pei: CMAR: Accurate and Efficient Classification Based on Multiple Class-
Association Rules. ICDM 2001: 369-376
Jinyan Li, Kotagiri Ramamohanarao, Guozhu Dong: Combining the Strength of Pattern Frequency and Distance
for Classification. PAKDD 2001: 455-466
Bing Liu, Wynne Hsu, Yiming Ma: Integrating Classification and Association Rule Mining. KDD 1998: 80-86
Kotagiri Ramamohanarao, James Bailey: Discovery of Emerging Patterns and Their Use in Classification.
Australian Conference on Artificial Intelligence 2003: 1-12
Ramamohanarao, K. and Bailey, J. and Fan, H. Efficient Mining of Contrast Patterns and Their Applications to
Classification, Third International Conference on Intelligent Sensing and Information Processing, 2005 (39--47).
Ramamohanarao, K. and Fan, H. Patterns Based Classifiers, World Wide Web 2007: 10(71--83).
Qun Sun, Xiuzhen Zhang, Kotagiri Ramamohanarao: Noise Tolerance of EP-Based Classifiers. Australian
Conference on Artificial Intelligence 2003: 796-806


Classification)
Xiaoxin Yin, Jiawei Han: CPAR: Classification based on Predictive Association Rules. SDM 2003
Xiuzhen Zhang, Guozhu Dong, Kotagiri Ramamohanarao: Information-Based Classification by Aggregating
Emerging Patterns. IDEAL 2000: 48-53
Xiuzhen Zhang, Guozhu Dong, Kotagiri Ramamohanarao: Building Behaviour Knowledge Space to Make
Classification Decision. PAKDD 2001: 488-494
Zhou Wang, Hongjian Fan, Kotagiri Ramamohanarao: Exploiting Maximal Emerging Patterns for Classification.
Australian Conference on Artificial Intelligence 2004: 1062-1068


Bibliography (Other Applications of Emerging Patterns)
Anne-Laure Boulesteix, Gerhard Tutz, Korbinian Strimmer: A CART-based approach to discover emerging
patterns in microarray data. Bioinformatics 19(18): 2465-2472 (2003).
Lijun Chen, Guozhu Dong: Masquerader Detection Using OCLEP: One Class Classification Using Length
Statistics of Emerging Patterns. Proceedings of International Workshop on INformation Processing over Evolving
Networks (WINPEN), 2006.
Guozhu Dong, Kaustubh Deshpande: Efficient Mining of Niches and Set Routines. PAKDD 2001: 234-246
Grandinetti, W.M. and Chesnevar, C.I. and Falappa, M.A. Enhanced Approximation of the Emerging Pattern
Space using an Incremental Approach, Proceedings of VII Workshop of Researchers in Computer Sciences,
Argentine, pp263--267, 2005
Jinyan Li, Huiqing Liu, See-Kiong Ng, Limsoon Wong. Discovery of Significant Rules for Classifying Cancer
Diagnosis Data . Bioinformatics. 19 (suppl. 2): ii93-ii102. (This paper was also presented in the 2003 European
Conference on Computational Biology, Paris, France, September 26-30.)
Jinyan Li, Huiqing Liu, James R. Downing, Allen Eng-Juh Yeoh, Limsoon Wong. Simple Rules Underlying Gene
Expression Profiles of More than Six Subtypes of Acute Lymphoblastic Leukemia (ALL) Patients. Bioinformatics.
19:71--78, 2003.
Jinyan Li, Limsoon Wong: Emerging patterns and gene expression data. Genome Informatics, 2001:12(3--13).
Jinyan Li, Limsoon Wong: Identifying good diagnostic gene groups from gene expression profiles using the
concept of emerging patterns. Bioinformatics 18(5): 725-734 (2002)
Jinyan Li, Limsoon Wong. Geography of Differences Between Two Classes of Data. Proceedings 6th European
Conference on Principles of Data Mining and Knowledge Discovery, pages 325--337, Helsinki, Finland, August
2002.
Jinyan Li and Limsoon Wong. Structural Geography of the space of emerging patterns. Intelligent Data Analysis
(IDA): An International Journal, Volume 9, pages 567-588, November 2005.
Jinyan Li, Xiuzhen Zhang, Guozhu Dong, Kotagiri Ramamohanarao, Qun Sun: Efficient Mining of High
Confidience Association Rules without Support Thresholds. PKDD 1999: 406-411


Bibliography (Other Applications of Emerging Patterns)
Shihong Mao, Guozhu Dong: Discovery of Highly Differentiative Gene Groups from Microarray Gene Expression
Data Using the Gene Club Approach. J. Bioinformatics and Computational Biology 3(6): 1263-1280 (2005).
Podraza, R. and Tomaszewski, K. KTDA: Emerging Patterns Based Data Analysis System, Proceedings of XXI
Fall Meeting of Polish Information Processing Society, pp213--221, 2005
Rioult, F. Mining strong emerging patterns in wide SAGE data, Proceedings of the ECML/PKDD Discovery
Challenge Workshop, Pisa, Italy, pp127--138, 2004
Eng-Juh Yeoh, Mary E. Ross, Sheila A. Shurtleff, W. Kent William, Divyen Patel, Rami Mahfouz, Fred G. Behm,
Susana C. Raimondi, Mary V. Reilling, Anami Patel, Cheng Cheng, Dario Campana, Dawn Wilkins, Xiaodong
Zhou, Jinyan Li, Huiqing Liu, Chin-Hon Pui, William E. Evans, Clayton Naeve, Limsoon Wong, James R.
Downing. Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia
by gene expression profiling. Cancer Cell, 1:133--143, March 2002.
Yoon, H.S. and Lee, S.H. and Kim, J.H. Application of Emerging Patterns for Multi-source Bio-Data Classification
and Analysis, LECTURE NOTES IN COMPUTER SCIENCE Vol 3610, 2005.
Yu, L.T.H. and Chung, F. and Chan, S.C.F. and Yuen, S.M.C. Using emerging pattern based projected
clustering and gene expression data for cancer detection, Proceedings of the second conference on Asia-Pacific
bioinformatics, pp75--84, 2004.
Zhang, X. and Dong, G. and Wong, L. Using CAEP to predict translation initiation sites from genomic DNA
sequences, TR2001/22, CSSE, Univ. of Melbourne, 2001.


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