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%.
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Park, L.A.F., Leckie, C.A., Ramamohanarao, K., Bezdek, J.C. (2009). Adapting Spectral Co-clustering to Documents and Terms Using Latent Semantic Analysis. In: Nicholson, A., Li, X. (eds) AI 2009: Advances in Artificial Intelligence. AI 2009. Lecture Notes in Computer Science(), vol 5866. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10439-8_31
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DOI: https://doi.org/10.1007/978-3-642-10439-8_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10438-1
Online ISBN: 978-3-642-10439-8
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