Application of the SpecHybrid Algorithm to text document clustering problem

In this paper, we present a relaxed version of the SpecHybrid Algorithm originally proposed for wireless cellular systems, and apply it to text document clustering problem. We conduct several experiments on two different datasets; a widely used benchmark dataset in English, and a Turkish textual dataset commonly used in text classification. Our results show that the proposed algorithm gives superior performance in text document clustering as compared to the standard k-means algorithm for any number of clusters while giving a comparable or better performance as compared to the standard EM algorithm for relatively large number of clusters depending on the similarity matrices used.