 Discovering optimal clusters using firefly algorithmAthraa Jasim Mohammed, Yuhanis Yusof and Husniza Husni International Journal of Data Mining, Modelling and Management, 2016, vol. 8, issue 4, 330-347 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. Keywords: partitional clustering; dynamic clustering; hierarchical clustering; text clustering; firefly algorithm; cluster discovering; optimal clusters; data clustering; bisect K-means clustering; general stochastic clustering. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijdmmm:v:8:y:2016:i:4:p:330-347 Access Statistics for this article More articles in International Journal of Data Mining, Modelling and Management from Inderscience Enterprises Ltd |
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