Abstract
In this paper, we present a text-clustering algorithm of frequent term set-based clustering (FTSC), which uses frequent term sets for texts clustering. This algorithm can reduce the dimensionality of the text data efficiently, thus it can improve accurate rate and running speed of the clustering algorithm. The results of clustering texts by the FTSC algorithm cannot reflect the overlap of texts' classes. Based on the FTSC algorithm, its improved algorithm - frequent term set-based hierarchical clustering algorithm (FTSHC) is given. This algorithm can determine the overlap of texts' classes by the overlap of frequent words sets, and provide an understandable description of the discovered clusters by the frequent terms sets. The experiment results prove that FTSC and FTSHC algorithms are more efficient than K-Means algorithm in the performance of clustering.
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