document: Frequent term-based text clustering Text clustering methods can be used to structure large sets of text or hypertext documents . The well-known methods of text clustering , however , do not really address the special problems of text clustering : very high dimensionality of the data , very large size of the databases and understandability of the cluster description . In this paper , we introduce a novel approach which uses frequent item ( term ) sets for text clustering . Such frequent sets can be efficiently discovered using algorithms for association rule mining . To cluster based on frequent term sets , we measure the mutual overlap of frequent sets with respect to the sets of supporting documents . We present two algorithms for frequent term-based text clustering , FTC which creates flat clusterings and HFTC for hierarchical clustering . An experimental evaluation on classical text documents as well as on web documents demonstrates that the proposed algorithms obtain clusterings of comparable quality significantly more efficiently than state-of-the - art text clustering algorithms . Furthermore , our methods provide an understandable description of the discovered clusters by their frequent term sets .
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