A cluster-based deviation detection task using the Artificial Bee Colony (ABC) algorithm

M. Faiza Abdulsalam, Azuraliza Abu Bakar

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

The Artificial Bee Colony (ABC) algorithm was motivated by the intelligent foraging behavior of honey bee swarms. The ABC algorithm was developed to solve clustering problems and revealed promising results in processing time and solution quality although, no research has yet considered employing ABC for deviation detection. In this study, researchers propose modifying the ABC clustering algorithm for deviation detection. An outlier factor has been used to identify the top n outliers that deviate from the dataset. The proposed algorithm was tested on three UCI benchmark datasets. Experimental results have shown that the ABC deviation detection algorithm has performed with comparable results.

Original languageEnglish
Pages (from-to)71-78
Number of pages8
JournalInternational Journal of Soft Computing
Volume7
Issue number2
Publication statusPublished - 2012

Fingerprint

Deviation
Outlier
Foraging
Swarm
Clustering algorithms
Clustering Algorithm
Clustering
Benchmark
Experimental Results
Processing

Keywords

  • Artificial bee colony (ABC) algorithm
  • Clustering
  • Clustering-based deviation detection
  • Deviation detection
  • Intelligent
  • Malaysia

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Modelling and Simulation

Cite this

A cluster-based deviation detection task using the Artificial Bee Colony (ABC) algorithm. / Faiza Abdulsalam, M.; Abu Bakar, Azuraliza.

In: International Journal of Soft Computing, Vol. 7, No. 2, 2012, p. 71-78.

Research output: Contribution to journalArticle

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