Data Mining based on Hashing Technique

International Journal of Trend in Scientific Research and Development, Volume 1(4), ISSN: 2456-6470
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IJTSRD | May-Jun 2017
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Data Mining based on Hashing Technique
Krishan Rohilla1
, Shabnam Kumari2
, Reema3
2,3
A.P., Department of CSE, Sat Kabir Institute of Technology & Management, Bahadurgarh, Haryana, India
1
M.Tech scholar., Deptt of CSE, Sat Kabir Institute of Technology & Management, Bahadurgarh, Haryana, India
Abstract: Data Mining is an important aspect for any
business. Most of the management level decisions are
based on the process of Data Mining. One of such
aspect is the association between different sale
products i.e. what is the actual support of a product
respected to the other product. This concept is called
Association Mining. According to this concept we
define the process of estimating the sale of one
product respective to the other product. We are
proposing an association rule based on the concept of
Hardware support. In this concept we first maintain
the database and compare it with systolic array after
this a pruning process is being performed to filter the
database and to remove the rarely used items. Finally
the data is indexed according to hashing technique
and the decision is performed in terms of support
count.
Keywords: Apriory, Clustering, Hashing, Data
mining Techniques, Decision Trees.
1. INTRODUCTION
Data mining refers to extracting or mining the
knowledge from large amount of data. Data collection
and storage technology has made it possible for
organizations to accumulate huge amounts of data at
lower cost. Exploiting this stored data, in order to
extract useful and actionable information, is the
overall goal of the generic activity termed as data
mining.
1.1.How does data mining work?
While large-scale information technology has been
evolving separate transaction and analytical systems,
data mining provides the link between the two. Data
mining software analyzes relationships and patterns in
stored transaction data based on open-ended user
queries. Several types of analytical software are
available: statistical, machine learning, and neural
networks. Generally, any of four types of
relationships are sought:
 Classes: Stored data is used to locate data in
predetermined groups. For example, a restaurant
chain could mine customer purchase data to
determine when customers visit and what they
typically order. This information could be used to
increase traffic by having daily specials.
 Clusters: Data items are grouped according to
logical relationships or consumer preferences. For
example, data can be mined to identify market
segments or consumer affinities.
 Associations: Data can be mined to identify
associations. The beer-diaper example is an
example of associative mining.
 Sequential patterns: Data is mined to anticipate
behavior patterns and trends. For example, an
outdoor equipment retailer could predict the
likelihood of a backpack being purchased based
on a consumer's purchase of sleeping bags and
hiking shoes.
1.2.Elements of data mining:
Data mining consists of five major elements:
 Extract, transform, and load transaction data
onto the data warehouse system.

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 Store and manage the data in a
multidimensional database system.
 Provide data access to business analysts and
information technology professionals.
 Analyze the data by application software.
 Present the data in a useful format, such as a
graph or table.
1.3.Parameters of Data Mining:
Data mining parameters include:
1.3.1. Regression - In statistics, regression analysis
includes any techniques for modeling and
analyzing several variables, when the focus is
on the relationship between a dependent
variable and one or more independent
variables
1.3.2. Sequence or path analysis - looking for
patterns where one event leads to another later
event.
1.3.3. Classification - looking for new patterns (May
result in a change in the way the data is
organized but that's ok).
1.3.4. Clustering - finding and visually documenting
groups of facts not previously known.
1.3.5. Decision Trees – Decision trees are commonly
used in operations research, specifically in
decision analysis, to help identify a strategy
most likely to reach a goal.
1.4.Levels of analysis:
Different levels of analysis are available:
 Artificial neural networks: Non-linear
predictive models that learn through training
and resemble biological neural networks in
structure.
 Genetic algorithms: Optimization techniques
that use processes such as genetic
combination, mutation, and natural selection
in a design based on the concepts of natural
evolution.
 Decision trees: Tree-shaped structures that
represent sets of decisions. These decisions
generate rules for the classification of a
dataset. Specific decision tree methods include
Classification and Regression Trees (CART)
and Chi Square Automatic Interaction
Detection (CHAID) . CART and CHAID are
decision tree techniques used for classification
of a dataset. They provide a set of rules that
you can apply to a new (unclassified) dataset
to predict which records will have a given
outcome. CART segments a dataset by
creating 2-way splits while CHAID segments
using chi square tests to create multi-way
splits. CART typically requires less data
preparation than CHAID.
 Nearest neighbor method: A technique that
classifies each record in a dataset based on a
combination of the classes of the k record(s)
most similar to it in a historical dataset (where
k 1). Sometimes called the k-nearest neighbor
technique.
 Rule induction: The extraction of useful if-
then rules from data based on statistical
significance.
 Data visualization: The visual interpretation
of complex relationships in multidimensional
data. Graphics tools are used to illustrate data
relationships
2. Architecture of Data Mining
To best apply advanced techniques, it must be
fully integrated with a data warehouse as well as
flexible interactive business analysis tools. Many
data mining tools currently operate outside of the
warehouse, requiring extra steps for extracting,
importing, and analyzing the data. Furthermore,
when new insights require operational
implementation, integration with the warehouse
simplifies the application of results from data
mining. The resulting analytic data warehouse can
be applied to improve business processes
throughout the organization, in areas such as
promotional campaign management, fraud
detection, new product rollout, and so on. Figure 1

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illustrates architecture for advanced analysis in a
large data warehouse.
Figure 1 - Integrated Data Mining Architecture
The ideal starting point is a data warehouse containing
a combination of internal data tracking all customer
contact coupled with external market data about
competitor activity. Background information on
potential customers also provides an excellent basis
for prospecting. This warehouse can be implemented
in a variety of relational database systems: Sybase,
Oracle, Redbrick, and so on, and should be optimized
for flexible and fast data access.
3. Problem Definition
Current researches on data mining are based on
simple transaction data models. Given an item set
{itemi} and a transaction set {transi}, an association
rule is defined as an implication of the form, XY,
where X and Y are non-overlap subsets of {itemi}. In
classification data set, an item can be viewed as
{attribute, value} pair. Two important related
quantities are confidence c, which is the percentage of
transactions including X and Y to transactions
including X, and support s, which is the percentage of
transactions including X and Y to all transactions.
Classification association rule (CAR) is then Xci
where ci is a class label. A training data set is such a
set of data items that for each item, there exists a class
label associated with it. A classifier is a function that
maps attributes to class labels. In general, given a
training data set, classification is to build a class
model from the training data set such that it can be
used to predict the class labels of unknown items with
high accuracy.
3.1.Association rule Mining:
Association rule mining finds interesting association
or correlation relationships among a large set of data
items. It first discovers frequent item sets satisfying
user-defined minimum support, and then from which
generates strong association rules satisfying user-
defined minimum confidence. The most famous
algorithm for association rule mining is Apriori
algorithm.[2]
Most of the previous studies on
association rule mining adopt the Apriori-like
candidate set generation-and-test approach.Apriori
algorithm uses frequent (k – 1)-itemsets to generate
candidate frequent k-itemsets and use database scan
and pattern matching to collect counts for the
candidate itemsets. Recently, J. Han et al critiqued
that the bottleneck of Apriori algorithm is the cost of
the candidate generation and multiple scans of
database. Han’s group developed another influential
method for discovering frequent pattern without
candidate generation, which is called frequent pattern
growth (FP-growth). It adopts divide-and-conquer
strategy and constructs a highly compact data
structure (FP-tree) to compress the original
transaction database. It focuses on the frequent pattern
(fragment) growth and eliminate repeated database
scan. The performance study by Han’s group shows
that FP-growth is more efficient than Aproiori
algorithm.[3]
3.2.Classification rule mining:
Classification rule mining is to build a class model or
classifier by analyzing predetermining training data
and apply the model to predict the future cases.
Besides other techniques for data classification such
as decision tree induction, Bayesian classification,
neural network, classification based on data
warehousing technology, and etc.The associative
classification or classification based on association
rules is an integrated technique that applies the
methods of association rule mining to the
classification. It typically consists of two steps:

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3.2.1. The first step finds the subset of association
rules that are both frequent and accurate using
association rule techniques.
3.2.2. The second step employs the rules for
classification.
4. Previous Work
Recent researches on the integration of association
rule mining and classification rule mining. Recently,
Bing Liu et al proposed Classification Based on
Association rules (CBA) algorithm as an integration
of classification rule mining and association rule
mining.[4]
The integration was done by finding a special subset
of association rules called class association rules
(CARs) and building a classifier from the CARs. The
main strength of CBA algorithm is its ability to use
the most accurate rules for classification, which
explains its better performance compared with some
original classification algorithms such as C4.5. Liu’s
research group also proposed some methods to deal
with the problems of the original CBA algorithm such
as single minimum support and not being able to
generate long rules for many datasets. The
performance of the algorithm was improved by using
multiple minimum support (Smin) instead of a single
Smin, and combining CBA algorithm with other
techniques such as decision tree method.[5,6]
More
recently, Wenmin Li et al critiqued some weakness of
Liu’s approach as follows: (1) simply selection a rule
with a maximal user-defined measure may affect the
classification accuracy, (2) the efficiency problem of
storing, retrieve, pruning, and sorting a large number
of rules for classification when there exist a huge
number of rules, large training data sets, and long
pattern rules. They proposed a new associative
classification algorithm: Classification based on
Multiple Association Rules (CMAR). The
experimental result shows that CMAR provides better
efficiency and accuracy compared with CBA
algorithm. The accuracy of CMAR is achieved by
using multiple association rules for classification. The
efficiency of CMAR is achieved by extension of
efficient frequent pattern method, FP-growth,
construction of a class distribution-associated FP-tree,
and applying a CR-tree structure to store and retrieve
mined association rules.[7]
(Both CBA algorithm and
CMAR algorithm will be discussed in detail later in
the section of related work.)
5. Proposed Work
6.
In this research work we are proposing a new
architecture for the association rule mining. The
complete concept the proposed work is based on two
main concepts
 Hash Based System
 Pipelined system
The system architecture is inspired from the hardware
enhancement. As the architecture is followed by any
hardware system same approach is being proposed in
this work to find the association between the selling
produces
The complete work is divided in 3 states:
 In first modules the data will be collected and
stored into the hardware system. In this system
the dataset is being compared with the systolic
array.
 In the second module the pruning process will
be performed. It is actual the filtration process
to clear all such items that are not part of
frequently used item list. We can setup the
association rules based on the frequently
selling items. If some item is being sold rarely
any need to establish any association rule onto
it. This process will be done by Pruning
 In third stage, on the dataset collected from the
customer transaction a hash table will be
maintained. On the basis of this dataset the
actual decision support will be calculated and
the results will be derived
7. Conclusion:

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DataIn this research we conclude that with the help of
hash based pipelining technique products in market
can be sold faster because in HAPPI technique it
removes bottleneck problem thereby providing faster
throughput and our sales process becomes faster
because due to indexing hasing process becomes
faster. Firstly items are kept in systolic array then
items which are not in close proximity with each other
are trimmed or removed from the filter then put into
hash table filter so that duplication of items get
removed so in this way. It solves our bottleneck
problem
Acknowledgement
I would like to thank my guide Ms. Shabnam Kumari
for her indispensible ideas and continuous support,
encouragement, advice and understanding me through
my difficult times and keeping up my enthusiasm,
encouraging me andfor showing great interest in my
dissertation work, this work could not finished
without her valuable comments and inspiring
guidance.
References:
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[4]http://www.slideshare.net/Annie05/sequential-
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Books:
[1]. Arun K. Pujari, Data Mining Techniques
[2]. Jiawei Han, Micheline Kamber, Data Mining:
Concepts and Techniques

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