Random Indexing K-tree

The purpose of this paper is to present and analyse the combination of Random Indexing (RI) with the K-tree algorithm. Both RI and K-tree adapt to changing data and decrease the cost of computationally intensive vector based applications. This combination is particularly suitable to the representation and clustering of very large document collections. Documents are typically represented in vector space as very sparse high dimensional vectors. RI can reduce the dimensionality and sparsity of this representation. In turn, the condensed representation is highly effective when working with K-tree. The paper is focused on determining the effectiveness of using RI with K-tree through experiments and comparative analysis of results. Sections 2 to 6 discuss K-tree, Random Indexing, Document Representation, Experimental Setup and Experimental results respectively. The paper ends with a conclusion in Section 7.