Data Mining and Knowledge Management

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Data Mining and Knowledge Management
Vinay Singh1, Kaushal Kumar2
1,2Research Scholar Department of Mechanical Engineering, GJUS&T, Hisar, Haryana, India
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Abstract - Rapid increases in technological and
informational systems have led businesses to collect customer
data in huge databases. Data mining is the process involving
analyzing, searching data to make it useful for human use.
Large amount of data is modeled, selected and explored in
order to determine comprehensible information. This article
represents data mining tools used to understand the data
mining process. Also, data mining enablers as well as barriers
are also described to make the subject more understandable.
Key Words: Datamining,Dataminingmodels,Knowledge
Management, Enablers, Barriers
1. INTRODUCTION
Data mining (DM) is the process of trawling through data to
find previously unknown relationships among the data that
are interesting to the user of the data (Hand, 1998). Data
Mining has been an established field (Fayyad et al., 1996;
Chen and Liu, 2005; Wang, 2005). Data miningistheprocess
of searching and analyzing data in order to find implicit, but
potentially useful, information (M.J.A. Berry et al, 1997). It
involves selecting, exploring and modeling large amounts of
data to uncover previously unknown patterns, and
ultimately comprehensible information, from large
databases (Shaw et al, 2001). Data mining uses a broad
family of computational methods that include statistical
analysis, decision trees, neural networks, rule inductionand
refinement, and graphic visualization (Brachman, 1996).
Also, Data Mining techniques shouldbecarefullyunderstood
and applied by the frontline users (Hall, 2004;Violino,2004;
King, 2005). Data mining allows a search, for valuable
information, in large volumes of data. The explosive growth
in databases has created a need to develop technologiesthat
use information and knowledge intelligently (Weiss &
Indurkhya, 1998). According to Rubenking (2001), "data
mining is the process of automatically extracting useful
information and relationships from immense quantities of
data. In its purest form, data mining doesn't involve looking
for specific information. Data mining is an interdisciplinary
field that combines artificial intelligence, database
management, data visualization, machine learning,
mathematic algorithms, and statistics. Data mining, also
known as knowledge discovery in databases (KDD) (Chen,
Han, & Yu, 1996; Fayyad, Piatetsky-Shapiro, & Smyth,
1996a), is a rapidly emerging field. Thistechnologyprovides
different methodologies for decision-making, problem
solving, analysis, planning, diagnosis, detection, integration,
prevention, learning, and innovation. Data Mining was
defined by Turban, Aronson, Liang, and Sharda (2007) as a
process that uses statistical, mathematical, artificial
intelligence and machine-learning techniques to extractand
identify useful information and subsequently gain
knowledge from large databases.
2. Data Mining and Knowledge Management
Knowledge discovery and learning is an iterative process
that extends the collection of data mining techniques into a
knowledge managementframework (Michael J.Shaw,2001).
Higher education will find larger and wider applications for
data mining than its counterpart in the business sector,
because higher education institutions carry three that data
mining intensive duties: scientificresearchthat relatesto the
creation of knowledge, teaching that concerns with the
transmission of knowledge, and institutional research that
pertains to the use of knowledge for decision making. All the
above tasks are well within the boundaries of Knowledge
Management, which drives the need for better and faster
decision making tools and methods(LuanJing,2005).Owing
to its strength, Data Mining is known as a powerful Business
Intelligence tool for knowledge discovery (Chen and Liu,
2005). The process of Data Mining is a Knowledge
Management process because it involves human knowledge
(Brachman et al., 1996).
Several authors have also written about the factors behind
the dawn of data mining. For instance, Therling (1995)
identified three reasons: The ease of data collection and
storage, the computing powerofmodernprocessors,andthe
need for fast and real time data mining. Yet, one important
reason absent from these is the growing interest in
Knowledge Management.
3. Data Mining Tools
a) Web-based software tools: To meet the competitive
global challenges, the firm’s knowledge workers require
improved tools for understanding the changingmarketsand
customer requirements. Historically, forecasting tools were
the primary business insight generation tools used to
analyze the competitive landscape (D.N. Clark, 1992). The
business objective for using these insight-generation tools
was to help knowledge workers predict the future of a given
market segment or the success of a particular product line.
These forecasting tools aided in reducing decision
uncertainty by providing a degree of confidence to those
decisions related to the success of market segments or
product lines (G.J. Browne et al, 1997).

b) Business model:The businessmodelsutilized bythe
knowledge workers can be categorized into assessment
models, tactical models, and strategic models. These
business models assist the knowledge worker in making
sense of the competitive landscape and in providing the
knowledge workers the needed focus (J.F. Courtney, 2001),(
B.T. Gale,1994).
c) The WEKA Data Mining Software: The Waikato
Environment for Knowledge Analysis (WEKA) came about
through the perceived need for a unified workbench that
would allow researchers easy access to state-of the- art
techniques in machine learning. It was envisioned that
WEKA would not only provide a toolbox of learning
algorithms, but also a framework inside which researchers
could implement new algorithms without having to be
concerned with supporting infrastructure for data
manipulation and schemeevaluation.(Mark Hall)Nowadays,
WEKA is recognized as a landmark system in data mining
and machine learning (G. Piatetsky, 2005).
d) Artificial neural network (ANN): Artificial neural
network (ANN) has popularity in solving several problems
and technical problems that involve prediction, and have a
wide ranging usage area is one of the most important data
mining techniques (K. Usha Rani, 2011). An artificial neural
network (ANN) is a computational model basedonbiological
neural networks and consists of an interconnected group of
artificial neurons. It can be treated as non-linear statistical
data modelling tools that can be used to model complex
relationships between inputs and outputsortofindpatterns
in data (M. Kamrunnahar, 2010).
4. Data Mining Models
a) Association: Associationaimstoestablishingrelationships
between items which exist together in a given record
(Ahmed, 2004; Jiao, Zhang, & Helander, 2006; Mitra et al.,
2002). Market basket analysis and cross selling programs
are typical examples for which association modelling is
usually adopted. Common tools for association modelling
are statistics and algorithms.
b) Classification: Classification is one of the most common
learning models in data mining (Ahmed, 2004; Berry &
Linoff, 2004; Carrier & Povel, 2003). It aims at building a
model to predict future customer behaviours through
classifying database records into a number of predefined
classes based on certain criteria (Ahmed,2004;Bersonet al.,
2000; Chen, Hsu, & Chou, 2003; Mitra et al., 2002). Common
tools used for classification are neural networks, decision
trees and if then-else rules.
c) Clustering: Clustering is the task of segmenting a
heterogeneous population into a number of more
homogenous clusters (Ahmed, 2004; Berry & Linoff, 2004;
Carrier & Povel, 2003; Mitra et al., 2002). It is different to
classification in that clusters are unknown at the time the
algorithm starts. Common tools for clusteringincludeneural
networks and discrimination analysis.
d) Forecasting: Forecasting estimates the futurevaluebased
on a record’s patterns. It deals with continuously valued
outcomes (Ahmed, 2004; Berry & Linoff, 2004). It relates to
modelling and the logical relationships of the model at some
time in the future. Demand forecast is a typical example of a
forecasting model. Common tools for forecasting include
neural networks and survival analysis.
e) Regression: Regression is a kind of statistical estimation
technique used to map each data object to a real value
provide prediction value (Carrier & Povel, 2003; Mitra et al.,
2002). Uses of regression include curve fitting, prediction
(including forecasting),modelingofcausal relationships, and
testing scientific hypotheses about relationships between
variables. Common tools for regression include linear
regression and logistic regression
f) Sequence discovery: Sequence discovery is the
identification of associations or patterns over time (Berson
et al., 2000; Carrier & Povel, 2003; Mitra et al., 2002).Itsgoal
is to model the states of the process generatingthesequence
or to extract and report deviation and trends over time
(Mitra et al., 2002). Common tools for sequence discovery
are statistics and set theory.
g) Visualization: Visualization refers to the presentation of
data so that users can view complex patterns (Shaw et al.,
2001). It is used in conjunction with other data mining
models to provide a clearer understanding of thediscovered
patterns or relationships (Turban et al., 2007). Examples of
visualization model are 3D graphs, ‘‘Hygraphs”and‘‘SeeNet”
(Shaw et al., 2001).
h) Summarization: It involves the finding a compact
description for a subset of data, e.g., the derivation of
summary or association rules and the use of multivariate
visualization techniques (Fayyad, Piatetsky-Shapiro, &
Smyth, 1996).
i)Dependency Modeling: This modeling includes finding a
model which describes significant dependencies between
variables (e.g., learning of belief networks)(Fayyad,
Piatetsky-Shapiro, & Smyth,1996).
j) Change and Deviation Detection: This process contributes
discovering the most significant changes in the data from
previously measured or normative values). (Fayyad,
Piatetsky- Shapiro, & Smyth, 1996).
Table -1: Enablers of Data Mining
Enablers of Data Mining
S. No Barriers Description References
1 Informati
on
Technolog
y
The advent of
informationtechnology
has transformed the
way marketing is done
and how companies
manage information
Kumar
et al.
2014,
Kumar et al.
2014

about their customers.
2 Internet
and the
World
Wide Web
The Internet and the
World Wide Web have
made the process of
collecting data easier,
adding to the volumeof
data available to
businesses
Michael J.
Shaw, C
Subramania
m Gek Woo
Tan Michael
E. Welge ,
2001
3 Effective
customer
relationsh
ip
managem
ent
Data mining tools can
help uncover the
hidden knowledge and
understand customer
better, while a
systematic knowledge
management effort can
channel the knowledge
into effective
marketing strategies.
D. Peppers,
M.Rogers,19
97
4 Developm
ents in
database
processin
g
Developments in
database processing,
data warehousing,
machine learning and
knowledge
management have
contributed greatly to
our understanding of
the data mining
process.
K.C.C.C.
Chan, A.K.C.
Wong,
1991, M.
Holsheimer,
M.L.
Kersten,
A.P.J. M.
Siebes,
1996,
5 Data
warehous
ing
Developments in
data warehousing,
knowledge
management have
the data mining
process.
W. Inmon,
1996,
6 Machine
learning
Developments in
data warehousing,
knowledge
management have
C.W.
Holsapple,
R. Pakath,
V.S. Jacob,
J.S. Zaveri,
1993,
the data mining
process.
7 Knowledg
e
managem
ent
Developments in
data warehousing,
knowledge
management have
the data mining
process.
D.M.
Amidon,
1998,H.
Holtz,
1992, M.C.
Rumizen,19
98
8 Advances
in
computer
hardware
and
software
Although, data mining
tools have been
available for a long
time, the advances in
computer hardware
and software,
particularly
exploratory tools like
data visualization and
neural networks, have
made data mining
more attractive and
practical.
U.M.Fayyad,
G. Piatetsky-
Shapiro, P.
Smyth,
1996
9 Strategic
business
initiatives
These initiatives
support applications
that help increase
efficiency and improve
effectiveness of the
firm to movingmassive
paper-based
information sources
into electronic form, to
facilitating data mining
and insight generation.
L. Bransten ,
1999
10 Knowledg
e
Discovery
in
Databases
It is the process of
using the database
along with any
required selection,
preprocessing, sub
sampling, and
transformations of it;
to apply data mining
methods (algorithms)
Fayyad,
Piatetsky-
Shapiro, &
Smyth
(1996).

to enumerate patterns
from it.
Table -2: Barriers of Data Mining
Barriers of Data Mining
S. No Barriers Description References
1
Intense
competiti
on
and
increased
choices
The intense
competition and
increased choices
available for
customers in market
have created new
pressures on
marketing decision-
makers and there has
emerged a need to
manage customers in
a long-term
relationship
D.Peppers,
M.Rogers,
1999
2
Technical
complexit
y issues
Technical complexity
issues, lack of senior
management focus,
inflexibility of the
software tools, and
difficulty in assessing
benefits provided to
the firm are the main
reasons to explain the
relatively low
implementation
success rate.
John H.
Heinrichs ,
Jeen-Su Lim,
2003
3
Lack of
senior
managem
ent focus
Lack of senior
management focus
produces relatively
low implementation
success rate and the
relatively low
satisfaction ratings
from these projects
G. Bassellier,
B.H. Reich, I.
Benbasat,
2001
4
Inflexibilit
y of the
software
tools
Inflexibility of the
softwaretoolshinders
in proper
implementation
success rate.
L.
Bransten,199
9
5
Difficulty
in
assessing
benefits
provided
to the
firm.
Tool industrysegment
continues to
experience a dramatic
40% compounded
annual sales growth
rate because of the
difficulty in assessing
the benefits provided
to the firm
John H.
Heinrichs ,
Jeen-Su Lim,
2003
6
Larger
databases
Databases with
hundreds of fieldsand
tables, millions of
records are quite
common. Methods for
dealing with large
data volumes include
more efficient
algorithms, sampling,
approximation
methods, and
massively parallel
processing
Agrawal et al.
1996,
Holsheimer
et al. 1996
7 High
dimensio
nality
There is often a very
large number of
records in the
database, and canalso
be a very large
number of fields
(attributes, variables)
so that the
dimensionality of the
problem is high.
Fayyad,
Piatetsky-
Shapiro, &
Smyth 1996
8 Overfittin
g
When the algorithm
searches for the best
parameters for one
particular model
using a limited set of
data, it may resulting
in poor performance
of the model on test
data. Possible
solutions include
cross-validation,
regularization, and
other sophisticated
statistical strategies.
Fayyad,
Piatetsky-
Shapiro, &
Smyth, 1996

9 Changing
data and
knowledg
e
Rapidly changing
(non-stationary) data
may make previously
discovered patterns
invalid. In addition,
the variables
measured in a given
application database
may be modified,
deleted,oraugmented
with new
measurements over
time.
Mannila,
Toivonen, &
Verkamo
1995;
Agrawal &
Psaila 1995,
Matheus,
Piatetsky-
Shapiro, and
Mc-Neill,
1996
10 Missing
and noisy
data
Important attributes
may be missing if the
database was not
designed with
discovery in mind.
Possible solutions
include more
sophisticated
statistical strategiesto
identify hidden
variables and
dependencies
Heckerman
1996; Smyth
et al. 1996
11
Complex
relationsh
ips
between
fields:
Hierarchically
structured attributes
or values, relations
between attributes,
and more
sophisticated means
for representing
knowledge about the
contents of a database
will require
algorithms that can
effectively utilizesuch
information.
Djoko, Cook,
& Holder
1995;
Dzeroski
1996
12 User
interactio
n and
prior
knowledg
e
Many current
Knowledge Discovery
in Database methods
and tools are not truly
interactiveandcannot
easily incorporate
prior knowledge
Cheeseman,
P. 1990
about a problem
except in simple ways
13
Integratio
n with
other
systems
A stand-alone
discovery system may
not be very useful.
Typical integration
issues include
integration with a
DBMS, integration
with spreadsheets
and visualization
tools, and
accommodating real-
time sensor readings.
Simoudis,
LivezeyKerbe
r 1995 and
Stolorz et al.
1995
7. CONCLUSIONS
The concept of data mining has been explained in the paper.
Data mining has been proved as a better tool for knowledge
discovery. Several data mining tools have been discussed in
the literature primarily includes: Web based software tool,
WEKA software tool and artificial neural network. Several
data mining models have also been discussed. Also, the
enablers and barriers of data mining process along with
their descriptions have also been discussed. Knowledge
management and data miningisalsocorrelatedinthearticle.
This article will help the academicians to get in depth
knowledge of the concept of data mining.
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