Mohammed, Athraa Jasim and Yusof, Yuhanis and Husni, Husniza (2016) Discovering optimal clusters using firefly algorithm. International Journal of Data Mining, Modelling and Management, 8 (4). p. 330. ISSN 1759-1163

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Abstract

Existing conventional clustering techniques require a pre-determined number of clusters, unluckily; missing information about real world problem makes it a hard challenge.A new orientation in data clustering is to automatically cluster a given set of items by identifying the appropriate number of clusters and the optimal centre for each cluster.In this paper, we present the WFA_selection algorithm that originates from weight-based firefly algorithm.The newly proposed WFA_selection merges selected clusters in order to produce a better quality of clusters.Experiments utilising the WFA and WFA_selection algorithms were conducted on the 20Newsgroups and Reuters-21578 benchmark dataset and the output were compared against bisect K-means and general stochastic clustering method (GSCM).Results demonstrate that the WFA_selection generates a more robust and compact clusters as compared to the WFA, bisect K-means and GSCM.

Item Type:	Article
Uncontrolled Keywords:	partitional clustering, dynamic clustering, hierarchical clustering, text clustering, firefly algorithm, cluster discovering, optimal clusters, data clustering, bisect K-means clustering, general stochastic clustering
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	School of Computing
Depositing User:	Dr. Yuhanis Yusof
Date Deposited:	18 Jan 2017 03:32
Last Modified:	18 Jan 2017 03:35
URI:	https://repo.uum.edu.my/id/eprint/20643

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