Discovering optimal clusters using firefly algorithm Online publication date: Sun, 01-Jan-2017
by Athraa Jasim Mohammed; Yuhanis Yusof; Husniza Husni
International Journal of Data Mining, Modelling and Management (IJDMMM), Vol. 8, No. 4, 2016
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.
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