FireflyClust: An automated hierarchical text clustering approach

Text clustering is one of the text mining tasks that is employed in search engines. discovering the optimal number of clusters for a dataset or repository is a challenging problem. various clustering algorithms have been reported in the literature but most of them rely on a pre-defined value of the k clusters. in this study,a variant of firefly algorithm,termed as fireflyclust,is proposed to automatically cluster text documents in a hierarchical manner. the proposed clustering method operates based on five phases: data pre-processing,clustering,item re-location,cluster selection and cluster refinement. experiments are undertaken based on different selections of threshold value. results on the trec collection named tr11,tr12,tr23 and tr45,showed that the fireflyclust is a better approach than the bisect k-means,hybrid bisect k-means and practical general stochastic clustering method. such a result would enlighten the directions in developing a better information retrieval engine for this dynamic and fast growing big data era. © 2017 penerbit utm press. all rights reserved.