Multi-objective PSO algorithm for mining numerical association rules without a priori discretization

Vahid Beiranvand, Mohamad Mobasher-Kashani, Azuraliza Abu Bakar

Research output: Contribution to journalArticle

42 Citations (Scopus)

Abstract

In the domain of association rules mining (ARM) discovering the rules for numerical attributes is still a challenging issue. Most of the popular approaches for numerical ARM require a priori data discretization to handle the numerical attributes. Moreover, in the process of discovering relations among data, often more than one objective (quality measure) is required, and in most cases, such objectives include conflicting measures. In such a situation, it is recommended to obtain the optimal trade-off between objectives. This paper deals with the numerical ARM problem using a multi-objective perspective by proposing a multi-objective particle swarm optimization algorithm (i.e., MOPAR) for numerical ARM that discovers numerical association rules (ARs) in only one single step. To identify more efficient ARs, several objectives are defined in the proposed multi-objective optimization approach, including confidence, comprehensibility, and interestingness. Finally, by using the Pareto optimality the best ARs are extracted. To deal with numerical attributes, we use rough values containing lower and upper bounds to show the intervals of attributes. In the experimental section of the paper, we analyze the effect of operators used in this study, compare our method to the most popular evolutionary-based proposals for ARM and present an analysis of the mined ARs. The results show that MOPAR extracts reliable (with confidence values close to 95%), comprehensible, and interesting numerical ARs when attaining the optimal trade-off between confidence, comprehensibility and interestingness.

Original languageEnglish
Pages (from-to)4259-4273
Number of pages15
JournalExpert Systems with Applications
Volume41
Issue number9
DOIs
Publication statusPublished - Jul 2014

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Association rules
Particle swarm optimization (PSO)
Multiobjective optimization

Keywords

  • Association rules
  • Data mining
  • Evolutionary algorithms
  • Multi-objective optimization

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Engineering(all)

Cite this

Multi-objective PSO algorithm for mining numerical association rules without a priori discretization. / Beiranvand, Vahid; Mobasher-Kashani, Mohamad; Abu Bakar, Azuraliza.

In: Expert Systems with Applications, Vol. 41, No. 9, 07.2014, p. 4259-4273.

Research output: Contribution to journalArticle

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