Adapting Spectral Co-clustering to Documents and Terms Using Latent Semantic Analysis

Abstract. Spectral co-clustering is a generic method of computing coclusters of relational data, such as sets of documents and their terms. Latent semantic analysis is a method of document and term smoothing that can assist in the information retrieval process. In this article we examine the process behind spectral clustering for documents and terms, and compare it to Latent Semantic Analysis. We show that both spectral co-clustering and LSA follow the same process, using different normalisation schemes and metrics. By combining the properties of the two co-clustering methods, we obtain an improved co-clustering method for document-term relational data that provides an increase in the cluster quality of 33.0%.

• Type: Conference
• Year: 2009
• Where: AUSAI
• Authors: Laurence A. F. Park, Christopher Leckie, Kotagiri Ramamohanarao, James C. Bezdek
• Keywords: Artificial Intelligence, AUSAI 2009, Latent Semantic Analysis, Relational Data, Spectral Co-clustering