A Graph-Based Ant Colony Optimization for Association Rule Mining

Ghassan Saleh Al-Dharhani, Zulaiha Ali Othman, Azuraliza Abu Bakar

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

This study is aimed at proposing a graph-based ant colony optimization (ACO) approach for association rule mining (ARM). The ACO-ARM is a two-phase approach comprising a Boolean transactional data representation scheme and the graph-based ACO. The first phase enhances the normal Apriori algorithm and engages in a data representation scheme. The data representation involves an adapted Boolean matrix representation of the transactional data. A standard Apriori algorithm is applied to the represented data, and n-frequent itemsets are generated. The second phase embellishes the ACO-ARM, which relies on the graph of 2-frequent items to generate the final frequent itemset. We have conducted two experiments. The outcomes of these tests reveal that the graph-based ACO-ARM enhances execution time compared to the standard Apriori algorithm. In addition, ACO-ARM improves the process of data representation in the Apriori algorithm.

Original languageEnglish
Pages (from-to)4651-4665
Number of pages15
JournalArabian Journal for Science and Engineering
Volume39
Issue number6
DOIs
Publication statusPublished - 2014

Fingerprint

Ant colony optimization
Association rules
Experiments

Keywords

  • Ant colony optimization
  • Association rule mining
  • Data representation
  • Frequent items
  • Graph-based

ASJC Scopus subject areas

  • General

Cite this

A Graph-Based Ant Colony Optimization for Association Rule Mining. / Al-Dharhani, Ghassan Saleh; Ali Othman, Zulaiha; Abu Bakar, Azuraliza.

In: Arabian Journal for Science and Engineering, Vol. 39, No. 6, 2014, p. 4651-4665.

Research output: Contribution to journalArticle

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