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

Authors:
Laurence A. Park;Christopher A. Leckie;Kotagiri Ramamohanarao;James C. Bezdek
Affiliations:
Department of Computer Science and Software Engineering, The University of Melbourne, Australia 3010;Department of Computer Science and Software Engineering, The University of Melbourne, Australia 3010;Department of Computer Science and Software Engineering, The University of Melbourne, Australia 3010;Department of Computer Science and Software Engineering, The University of Melbourne, Australia 3010
Venue:
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Year:
2009

Citing 4
Cited 1

A probabilistic model of information retrieval: development and comparative experiments

Information Processing and Management: an International Journal
Co-clustering documents and words using bipartite spectral graph partitioning

Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Kernel k-means: spectral clustering and normalized cuts

Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Term norm distribution and its effects on latent semantic indexing

Information Processing and Management: an International Journal

Approximate document outlier detection using random spectral projection

AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

Spectral co-clustering is a generic method of computing co-clusters 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%.