چکیده (انگلیسی):

Document clustering is widely used in Information Retrieval
however, existing clustering techniques suffer from local optima problem in
determining the k number of clusters. Various efforts have been put to
address such drawback and this includes the utilization of swarm-based
algorithms such as particle swarm optimization and Ant Colony
Optimization. This study explores the adaptation of another swarm
algorithm which is the Firefly Algorithm (FA) in text clustering. We
present two variants of FA; Weight- based Firefly Algorithm (WFA) and
Weight-based Firefly Algorithm II (WFAII). The difference between the
two algorithms is that the WFAII, includes a more restricted condition in
determining members of a cluster. The proposed FA methods are later
evaluated using the 20Newsgroups dataset. Experimental results on the
quality of clustering between the two FA variants are presented and are
later compared against the one produced by particle swarm optimization,
K-means and the hybrid of FA and -K-means. The obtained results
demonstrated that the WFAII outperformed the WFA, PSO, K-means and
FA-Kmeans. This result indicates that a better clustering can be obtained
once the exploitation of a search solution is improved.