Data mining techniques

IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 02 Issue: 11 | Nov-2013, Available @ http://www.ijret.org 116
DATA MINING TECHNIQUES: A SURVEY PAPER
Nikita Jain1
, Vishal Srivastava2
1
M. Tech. Scholar, 2
Associate Professor, Arya College of Engineering and IT, Rajasthan, India,
nikitagoodjain@gmail.com, vishal500371@yahoo.co.in
Abstract
In this paper, the concept of data mining was summarized and its significance towards its methodologies was illustrated. The data
mining based on Neural Network and Genetic Algorithm is researched in detail and the key technology and ways to achieve the data
mining on Neural Network and Genetic Algorithm are also surveyed. This paper also conducts a formal review of the area of rule
extraction from ANN and GA.
Keywords: Data Mining, Neural Network, Genetic Algorithm, Rule Extraction.
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1. INTRODUCTION
Data mining refers to extracting or mining the knowledge from
large amount of data. The term data mining is appropriately
named as „Knowledge mining from data‟ or “Knowledge
mining”.
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. The following definition is
given:
Data mining is the process of exploration and analysis, by
automatic or semiautomatic means, of large quantities of data
in order to discover meaningful patterns and rules.
In [1], the following definition is given:
Data mining is the process of exploration and analysis, by
automatic or semiautomatic means, of large quantities of data
in order to discover meaningful patterns and rules.
Data mining is an interdisciplinary subfield of computer
science which involves computational process of large data
sets‟ patterns discovery. The goal of this advanced analysis
process is to extract information from a data set and transform
it into an understandable structure for further use. The methods
used are at the juncture of artificial intelligence, machine
learning, statistics, database systems and business intelligence.
Data Mining is about solving problems by analyzing data
already present in databases [2].
Data mining is also stated as essential process where intelligent
methods are applied in order to extract the data patterns.
Data mining consists of five major elements:
 Extract, transform, and load transaction data onto the
data warehouse system.
 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.
Data mining functionalities are used to specify the kind of
patterns to be found in data mining tasks. Data mining tasks
can be classified in two categories-descriptive and predictive.
Descriptive mining tasks characterize the general properties of
the data in database. Predictive mining tasks perform inference
on the current data in order to make predictions.
The purpose of a data mining effort is normally either to create
a descriptive model or a predictive model. A descriptive
model presents, in concise form, the main characteristics of the
data set. It is essentially a summary of the data points, making
it possible to study important aspects of the data set. Typically,
a descriptive model is found through undirected data mining;
i.e. a bottom-up approach where the data “speaks for itself”.
Undirected data mining finds patterns in the data set but leaves
the interpretation of the patterns to the data miner. The purpose
of a predictive model is to allow the data miner to predict an
unknown (often future) value of a specific variable; the target
variable. If the target value is one of a predefined number of
discrete (class) labels, the data mining task is called
classification. If the target variable is a real number, the task is
regression.
The predictive model is thus created from given known values
of variables, possibly including previous values of the target
variable. The training data consists of pairs of measurements,

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each consisting of an input vector x (i) with a corresponding
target value y(i). The predictive model is an estimation of the
function y=f(x; q) able to predict a value y, given an input
vector of measured values x and a set of estimated parameters q
for the model f. The process of finding the best q values is the
core of the data mining technique [3].
At the core of the data mining process is the use of a data
mining technique. Some data mining techniques directly obtain
the information by performing a descriptive partitioning of the
data. More often, however, data mining techniques utilize
stored data in order to build predictive models. From a general
perspective, there is strong agreement among both researchers
and executives about the criteria that all data mining techniques
must meet. Most importantly, the techniques should have high
performance. This criterion is, for predictive modelling,
understood to mean that the technique should produce models
that will generalize well, i.e. models having high accuracy
when performing predictions based on novel data.
Classification and prediction are two forms of data analysis that
can be used to extract models describing the important data
classes or to predict the future data trends. Such analysis can
help to provide us with a better understanding of the data at
large. The classification predicts categorical (discrete,
unordered) labels, prediction model, and continuous valued
function.
2. METHODOLOGIES OF DATA MINING
2.1Neural Network
Neural Network or an artificial neural network is a biological
system that detects patterns and makes predictions. The greatest
breakthroughs in neural network in recent years are in their
application to real world problems like customer response
prediction, fraud detection etc. Data mining techniques such as
neural networks are able to model the relationships that exist in
data collections and can therefore be used for increasing
business intelligence across a variety of business applications
[4]. This powerful predictive modelling technique creates very
complex models that are really difficult to understand by even
experts. Neural Networks are used in a variety of applications.
It is shown in fig.1. Artificial neural network have become a
powerful tool in tasks like pattern recognition, decision
problem or predication applications. It is one of the newest
signals processing technology. ANN is an adaptive, non linear
system that learns to perform a function from data and that
adaptive phase is normally training phase where system
parameter is change during operations. After the training is
complete the parameter are fixed. If there are lots of data and
problem is poorly understandable then using ANN model is
accurate, the non linear characteristics of ANN provide it lots
of flexibility to achieve input output map. Artificial Neural
Networks, provide user the capabilities to select the network
topology, performance parameter, learning rule and stopping
criteria.
Fig: 1 Neural Network with hidden layers
2.2 Decision Trees
A decision tree is a flow chart like structure where each node
denotes a test on an attribute value, each branch represents an
outcome of the test and tree leaves represent classes or class
distribution. A decision tree is a predictive model most often
used for classification. Decision trees partition the input space
into cells where each cell belongs to one class. The partitioning
is represented as a sequence of tests. Each interior node in the
decision tree tests the value of some input variable, and the
branches from the node are labelled with the possible results of
the test. The leaf nodes represent the cells and specify the class
to return if that leaf node is reached. The classification of a
specific input instance is thus performed by starting at the root
node and, depending on the results of the tests, following the
appropriate branches until a leaf node is reached [5].Decision
tree is represented in figure 2.
Fig 2 Decision tree
Decision tree is a predictive model that can be viewed as a tree
where each branch of the tree is a classification question and
leaves represent the partition of the data set with their
classification. The author defines a Decision Tree as a

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schematic tree-shaped diagram used to determine a course of
action or show a statistical probability [6]. Decision trees can
be viewed from the business perspective as creating a
segmentation of the original data set. Thus marketing managers
make use of segmentation of customers, products and sales
region for predictive study. These predictive segments derived
from the decision tree also come with a description of the
characteristics that define the predictive segment. Because of
their tree structure and skill to easily generate rules the method
is a favoured technique for building understandable models.
2.3 Genetic Algorithm
Genetic Algorithm attempt to incorporate ideas of natural
evaluation The general idea behind GAs is that we can build a
better solution if we somehow combine the "good" parts of
other solutions (schemata theory), just like nature does by
combining the DNA of living beings [7].
Genetic Algorithm is basically used as a problem solving
strategy in order to provide with a optimal solution. They are
the best way to solve the problem for which little is known.
They will work well in any search space because they form a
very general algorithm. The only thing to be known is what the
particular situation is where the solution performs very well,
and a genetic algorithm will generate a high quality solution.
Genetic algorithms use the principles of selection and evolution
to produce several solutions to a given problem. It is shown in
fig. 3.
Fig 3: Structural view of Genetic Algorithm
Genetic algorithms (GAs) [8] are based on a biological
applications; it depends on theory of evolution. When GAs are
used for problem solving, the solution has three distinct stages:
 The solutions of the problem are encoded into
representations that support the necessary variation and
selection operations; these representations, are called
chromosomes, are as simple as bit strings.
 A fitness function judges which solutions are the “best”
life forms, that is, most appropriate for the solution of
the particular problem. These individuals are favoured in
survival and reproduction, thus giving rise to generation.
Crossover and mutation produce a new generation of
individuals by recombining features of their parents. Eventually
a generation of individuals will be interpreted back to the
original problem domain and the fit individual represents the
solution.
2.4 Rule Extraction
The taxonomy of Rule extraction contains three main criteria
for evaluation of algorithms: the scope of use, the type of
dependency on the black box and the format of the extract
description. The first dimension concerns with the scope of use
of an algorithm either regression or classification. The second
dimension focuses on the extraction algorithm on the
underlying black-box: independent versus dependent
algorithms. The third criterion focuses on the obtained rules
that might be worthwhile: predictive versus descriptive
algorithms. Besides this taxonomy the evaluation criteria
appears in almost all of these surveys-Quality of the extracted
rule; Scalability of the algorithm; consistency of the algorithm
[9].
Generally a rule consists of two values. A left hand antecedent
and a right hand consequent. An antecedent can have one or
multiple conditions which must be true in order for the
consequent to be true for a given accuracy whereas a
consequent is just a single condition. Thu s while mining a rule
from a database antecedent, consequent, accuracy, and
coverage are all targeted. Sometimes “interestingness” is also
targeted used for ranking. The situation occurs when rules have
high coverage and accuracy but deviate from standards. It is
also essential to note that even though patterns are produced
from rule induction system, they all not necessarily mean that a
left hand side (“if “part) should cause the right hand side
(“then”) part to happen. Once rules are created and
interestingness is checked they can be used for predictions in
business where each rule performs a prediction keeping a
consequent as the target and the accuracy of the rule as the
accuracy of the prediction which gives an opportunity for the
overall system to improve and perform well.
For data mining domain, the lack of explanation facilities
seems to be a serious drawback as it produce opaque model,
along with that accuracy is also required. To remove the
deficiency of ANN and decision tree, we suggest rule
extraction to produce a transparent model along with accuracy.
It is becoming increasingly apparent that the absence of an
explanation capability in ANN systems limits the realizations
of the full potential of such systems, and it is this precise
deficiency that the rule extraction process seeks to reduce [10].
Experience from the field of expert systems has shown that an
explanation capability is a vital function provided by symbolic
AI systems. In particular, the ability to generate even limited

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explanations is absolutely crucial for user acceptance of such
systems. Since the purpose of most data mining systems is to
support decision making, the need for explanation facilities in
these systems is apparent. But many systems must be regarded
as black boxes; i.e. they are opaque to the user.
For the rules to be useful there are two pieces of information
that must be supplied as well as the actual rule:
 Accuracy - How often is the rule correct?
 Coverage - How often does the rule apply?
Only because the pattern in the data base is expressed as rule, it
does not mean that it is true always. So like data mining
algorithms it is equally important to identify and make obvious
the uncertainty in the rule. This is called accuracy. The
coverage of the rule means how much of the database it
“covers” or applies to.
Craven and Shavlik in there paper [11] listed five criteria for
rule extraction, and they are as follows:
 Comprehensibility: The extent to which extracted
representations are humanly comprehensible.
 Fidelity: The extent to which extracted representations
accurately model the networks from which they were
extracted.
 Accuracy: The ability of extracted representations to
make accurate predictions on previously unseen cases.
 Scalability: The ability of the method to scale to
networks with large input spaces and large numbers of
weighted connections.
 Generality: The extent to which the method requires
special training.
CONCLUSIONS
If the conception of computer algorithms being based on the
evolutionary of the organism is surprising, the extensiveness
with which these methodologies are applied in so many areas is
no less than astonishing. At present data mining is a new and
important area of research and ANN itself is a very suitable for
solving the problems of data mining because its characteristics
of good robustness, self-organizing adaptive, parallel
processing, distributed storage and high degree of fault
tolerance. The commercial, educational and scientific
applications are increasingly dependent on these
methodologies.
REFERENCES
[1] Xingquan Zhu, Ian Davidson, “Knowledge Discovery
and Data Mining: Challenges and Realities”, ISBN 978-
1-59904-252, Hershey, New York, 2007.
[2] Joseph, Zernik, “Data Mining as a Civic Duty – Online
Public Prisoners Registration Systems”, International
Journal on Social Media: Monitoring,
Measurement, Mining, vol. - 1, no.-1, pp. 84-96,
September2010.
[3] Zhao, Kaidi and Liu, Bing, Tirpark, Thomas M. and
Weimin, Xiao,”A Visual Data Mining Framework for
Convenient Identification of Useful Knowledge”, ICDM
'05 Proceedings of the Fifth IEEE International
Conference on Data Mining, vol.-1, no.-1,pp.- 530-
537,Dec 2005.
[4] R. Andrews, J. Diederich, A. B. Tickle,” A survey and
critique of techniques for extracting rules from trained
artificial neural networks”, Knowledge-Based
Systems,vol.- 8,no.-6, pp.-378-389,1995.
[5] Lior Rokach and Oded Maimon,“Data Mining with
Decision Trees: Theory and Applications(Series in
Machine Perception and Artificial Intelligence)”, ISBN:
981-2771-719, World Scientific Publishing Company, ,
2008.
[6] Venkatadri.M and Lokanatha C. Reddy ,“A comparative
study on decision tree classification algorithm in data
mining” , International Journal Of Computer
Applications In Engineering ,Technology And Sciences
(IJCAETS), Vol.- 2 ,no.- 2 , pp. 24- 29 , Sept 2010.
[7] AnkitaAgarwal,”Secret Key Encryption algorithm using
genetic algorithm”, vol.-2, no.-4, ISSN: 2277 128X,
IJARCSSE, pp. 57-61, April 2012.
[8] Li Lin, Longbing Cao, Jiaqi Wang, Chengqi Zhang,
“The Applications of Genetic Algorithms in Stock
Market Data Mining Optimisation”, Proceedings of Fifth
International Conference on Data Mining, Text Mining
and their Business Applications,pp- 593-604,sept 2005.
[9] Fu Xiuju and Lipo Wang “Rule Extraction from an RBF
Classifier Based on Class-Dependent Features “,
ISNN‟05 Proceedings of the Second international
conference on Advances in Neural Networks ,vol.-1,pp.-
682-687,2005.
[10] H. Johan, B. Bart and V. Jan, “Using Rule Extraction to
Improve the Comprehensibility of Predictive Models”.
In Open Access publication from Katholieke Universiteit
Leuven, pp.1-56, 2006
[11] M. Craven and J. Shavlik, “Learning rules using ANN “,
Proceeding of 10th
International Conference on Machine
Learning, pp.-73-80, July 1993.
BIOGRAPHIES
Nikita Jain Pursuing M. Tech. in Computer
Science She has published many national and
international research papers.
Mr. Vishal Shrivastava working as Assistant
Professor in Arya College & IT He has
published many national and international
research papers. He has very depth knowledge
of his research areas

Data mining techniques

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