ABSTRACT

Text clustering methods can be used to group large sets of text documents. Most of the text clustering methods do not address the problems of text clustering such as very high dimensionality of the data and understandability of the clustering descriptions. In this paper, a frequent term based approach of clustering has been introduced; it provides a natural way of reducing a large dimensionality of the document vector space. This approach is based on clustering the low dimensionality frequent term sets and not on clustering high dimensionality vector space. Four algorithms for effective term based text clustering has been presented. An experimental evaluation on classical text documents as well as on web documents demonstrates that the proposed algorithms obtain clustering of comparable quality significantly more efficient than existing text clustering algorithms.