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Parallel multi-objective genetic algorithms for associative classification rule mining

Authors:

B. S. P. Mishra,

A. K. Addy,

R. Roy,

S. DehuriAuthors Info & Claims

ICCCS '11: Proceedings of the 2011 International Conference on Communication, Computing & Security

Pages 409 - 414

https://doi.org/10.1145/1947940.1948025

Published: 12 February 2011 Publication History

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Abstract

Association and classification rule mining are two well-known techniques used in data mining. The integrated approach is known as associative classification rule mining (ACRM), which has helped in developing a compact and efficient classifier for the classification of unknown samples. In this paper, we treated the ACRM as a multi-objective problem and applied the Parallel Multi-objective Genetic Algorithms (PMOGAs) to solve it. ACRM is associated with two phases like rule extraction and rule selection. As ACRM is a multi-objective problem so by applying PMOGA on it we can optimize the measures like support and confidence of association rule mining to extract classification rules in rule extraction phase and in rule selection phase a small number of rules are targeted from the extracted rules to design an accurate and compact classifier, which can maximize the accuracy of the rule set and minimize their complexity. Experiments are conducted on UCI data set by using MOGA and PMOGA. Finally the computational results are analyzed and concluded that the PMOGA for multi-objective rule selection generates a Pareto optimal rule sets with a compact set of classification rules in less time vis-a-vis to MOGA without severely degrading their classification accuracy.

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Cited By

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Kumar MSrivastava S(2016)Rules Extraction using Data Mining in Historical DataBusiness Intelligence10.4018/978-1-4666-9562-7.ch014(263-279)Online publication date: 2016
https://doi.org/10.4018/978-1-4666-9562-7.ch014
Djenouri YBendjoudi AMehdi MNouali-Taboudjemat NHabbas Z(2015)GPU-based bees swarm optimization for association rules miningThe Journal of Supercomputing10.1007/s11227-014-1366-871:4(1318-1344)Online publication date: 1-Apr-2015
https://dl.acm.org/doi/10.1007/s11227-014-1366-8
Kumar MSrivastava S(2014)Rules Extraction using Data Mining in Historical DataData Mining and Analysis in the Engineering Field10.4018/978-1-4666-6086-1.ch006(89-106)Online publication date: 2014
https://doi.org/10.4018/978-1-4666-6086-1.ch006
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Index Terms

Parallel multi-objective genetic algorithms for associative classification rule mining
1. Information systems
  1. Data management systems
    1. Database management system engines
      1. Parallel and distributed DBMSs
  2. Information systems applications
    1. Data mining
2. Theory of computation

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Information

Published In

ICCCS '11: Proceedings of the 2011 International Conference on Communication, Computing & Security

February 2011

656 pages

ISBN:9781450304641

DOI:10.1145/1947940

General Chairs:
Sanjay Kumar Jena
NIT Rourkela, India
,
Rajeev Kumar
IIT Kharagpur, India
,
Program Chairs:
Ashok Kumar Turuk
NIT Rourkela, India
,
Manoranjan Dash
NTU, Singapore

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 February 2011

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ICCCS '11

ICCCS '11: International Conference on Communication, Computing & Security

February 12 - 14, 2011

Odisha, Rourkela, India

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Kumar MSrivastava S(2016)Rules Extraction using Data Mining in Historical DataBusiness Intelligence10.4018/978-1-4666-9562-7.ch014(263-279)Online publication date: 2016
https://doi.org/10.4018/978-1-4666-9562-7.ch014
Djenouri YBendjoudi AMehdi MNouali-Taboudjemat NHabbas Z(2015)GPU-based bees swarm optimization for association rules miningThe Journal of Supercomputing10.1007/s11227-014-1366-871:4(1318-1344)Online publication date: 1-Apr-2015
https://dl.acm.org/doi/10.1007/s11227-014-1366-8
Kumar MSrivastava S(2014)Rules Extraction using Data Mining in Historical DataData Mining and Analysis in the Engineering Field10.4018/978-1-4666-6086-1.ch006(89-106)Online publication date: 2014
https://doi.org/10.4018/978-1-4666-6086-1.ch006
Dehuri SGhosh A(2013)Revisiting evolutionary algorithms in feature selection and nonfuzzy/fuzzy rule based classificationWIREs Data Mining and Knowledge Discovery10.1002/widm.10873:2(83-108)Online publication date: 20-Feb-2013
https://doi.org/10.1002/widm.1087
Mishra BDehuri SMall RGhosh A(2011)Parallel Single and Multiple Objectives Genetic AlgorithmsInternational Journal of Applied Evolutionary Computation10.4018/jaec.20110401022:2(21-57)Online publication date: 1-Apr-2011
https://dl.acm.org/doi/10.4018/jaec.2011040102

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A New Approach to Associative Classification Based on Binary Multi-objective Particle Swarm Optimization

Mining numerical association rules via multi-objective genetic algorithms

Multi-objective rule mining using genetic algorithms