Mohammed Jabbar, Ayad and Ku-Mahamud, Ku Ruhana and Sagban, Rafid (2020) An improved ACS algorithm for data clustering. Indonesian Journal of Electrical Engineering and Computer Science, 17 (3). pp. 1506-1515. ISSN 2502-4752

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Abstract

Data clustering is a data mining technique that discovers hidden patterns by creating groups (clusters) of objects. Each object in every cluster exhibits sufficient similarity to its neighbourhood, whereas objects with insufficient similarity are found in other clusters. Data clustering techniques minimise intra-cluster similarity in each cluster and maximise inter-cluster dissimilarity amongst different clusters. Ant colony optimisation for clustering (ACOC) is a swarm algorithm inspired by the foraging behaviour of ants. This algorithm minimises deterministic imperfections in which clustering is considered an optimisation problem. However, ACOC suffers from high diversification in which the algorithm cannot search for best solutions in the local neighbourhood. To improve the ACOC, this study proposes a modified ACOC, called M-ACOC, which has a modification rate parameter that controls the convergence of the algorithm. Comparison of the performance of several common clustering algorithms using real-world datasets shows that the accuracy results of the proposed algorithm surpasses other algorithms.

Item Type:	Article
Uncontrolled Keywords:	Ant colony optimisation, Data clustering,Data mining, Optimisation based-clustering, Swarm intelligence.
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	School of Computing
Depositing User:	Mrs. Norazmilah Yaakub
Date Deposited:	27 Jul 2020 03:03
Last Modified:	27 Jul 2020 03:03
URI:	https://repo.uum.edu.my/id/eprint/27277

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