  Document clustering as an unsupervised approach extensively used to navigate,
filter, summarize and manage large collection of document repositories like the
World Wide Web (WWW). Recently, focuses in this domain shifted from traditional
vector based document similarity for clustering to suffix tree based document
similarity, as it offers more semantic representation of the text present in
the document. In this paper, we compare and contrast two recently introduced
approaches to document clustering based on suffix tree data model. The first is
an Efficient Phrase based document clustering, which extracts phrases from
documents to form compact document representation and uses a similarity measure
based on common suffix tree to cluster the documents. The second approach is a
frequent word/word meaning sequence based document clustering, it similarly
extracts the common word sequence from the document and uses the common
sequence/ common word meaning sequence to perform the compact representation,
and finally, it uses document clustering approach to cluster the compact
documents. These algorithms are using agglomerative hierarchical document
clustering to perform the actual clustering step, the difference in these
approaches are mainly based on extraction of phrases, model representation as a
compact document, and the similarity measures used for clustering. This paper
investigates the computational aspect of the two algorithms, and the quality of
results they produced.
