An Efficient Document Clustering Algorithm based on the Topic Binder Hypothesis

We present an efficient document clustering algorithm that uses a term frequency vector for each document instead of using a huge proximity matrix. The algorithm has the following features: 1) it consumes relatively little memory space and runs fast, 2) it produces a hierarchy in the form of a document classification tree, and 3) the hierarchy obtained by the algorithm explicitly reveals the collection structure. We confirm these features and thus show the algorithm's feasibility with clustering experiments in which we use two collections of Japanese documents, the sizes of which are 83,099 and 14,701. We also introduce two applications of this algorithm. 1 Motivation Document clustering has long received keen attention from those concerned with document retrieval, and some of the many papers are those of (van Rijsbergen, 1986), (Croft, 1980) and (Griffiths et al., 1984) which have studied the document clustering techniques for retrieval purposes. The research interest has mainly con..