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Mining Comprehensible and Interesting Rules: A Genetic Algorithm Approach

by Jyoti Vashishtha, Dharminder Kumar, Saroj Ratnoo, Kapila Kundu
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 31 - Number 1
Year of Publication: 2011
Authors: Jyoti Vashishtha, Dharminder Kumar, Saroj Ratnoo, Kapila Kundu
10.5120/3792-5221

Jyoti Vashishtha, Dharminder Kumar, Saroj Ratnoo, Kapila Kundu . Mining Comprehensible and Interesting Rules: A Genetic Algorithm Approach. International Journal of Computer Applications. 31, 1 ( October 2011), 39-47. DOI=10.5120/3792-5221

@article{ 10.5120/3792-5221,
author = { Jyoti Vashishtha, Dharminder Kumar, Saroj Ratnoo, Kapila Kundu },
title = { Mining Comprehensible and Interesting Rules: A Genetic Algorithm Approach },
journal = { International Journal of Computer Applications },
issue_date = { October 2011 },
volume = { 31 },
number = { 1 },
month = { October },
year = { 2011 },
issn = { 0975-8887 },
pages = { 39-47 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume31/number1/3792-5221/ },
doi = { 10.5120/3792-5221 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:17:01.826775+05:30
%A Jyoti Vashishtha
%A Dharminder Kumar
%A Saroj Ratnoo
%A Kapila Kundu
%T Mining Comprehensible and Interesting Rules: A Genetic Algorithm Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 31
%N 1
%P 39-47
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A majority of contribution in the domain of rule mining overemphasize on maximizing the predictive accuracy of the discovered patterns. The user-oriented criteria such as comprehensibility and interestingness are have been given secondary importance. Recently, it has been widely acknowledged that even highly accurate discovered knowledge might be worthless if it scores low on the qualitative parameters of comprehensibility and interestingness. This paper presents a classification algorithm based on evolutionary approach that discovers comprehensible and interesting in CNF form in which along with conjunction in between various attributes there is disjunction among the values of an attribute. A flexible encoding scheme, genetic operators with appropriate syntactic constraints and a suitable fitness function to measure the goodness of rules are proposed for effective evolution of rule sets. The proposed genetic algorithm is validated on several datasets of UCI data set repository and experimental results are presented which clearly indicate lower error rates and more comprehensibility across a range of datasets. Some of the rules show the interesting and valuable nuggets of knowledge discovered from small disjuncts of high accuracy and low support which are very difficult to capture otherwise.

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Index Terms

Computer Science
Information Sciences

Keywords

Comprehensibility interestingness classification rules genetic algorithm

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