Applying Semantic Suffix Net to suffix tree clustering

In this paper we consider the problem of clustering snippets returned from search engines. We propose a technique to invoke semantic similarity in the clustering process. Our technique improves on the well-known STC method, which is a highly efficient heuristic for clustering web search results. However, a weakness of STC is that it cannot cluster semantic similar documents. To solve this problem, we propose a new data structure to represent suffixes of a single string, called a Semantic Suffix Net (SSN). A generalized semantic suffix net is created to represent suffixes of a set of strings by using a new operator to partially combine nets. A key feature of this new operator is to find a joint point by using semantic similarity and string matching; net pairs combination then begins at that joint point. This logic causes the number of nodes and branches of a generalized semantic suffix net to decrease. The operator then uses the line of suffix links as a boundary to separate the net. A generalized semantic suffix net is then incorporated into the STC algorithm so that it can cluster semantically similar snippets. Experimental results show that the proposed algorithm improves upon conventional STC.