PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Clustering of contingency table with Poisson block mixture model
Gérard Govaert and Mohamed Nadif
In: CLADAG 05, 6-8 June 2005, Parma, Italy.

Abstract

Block clustering or simultaneous clustering has become an important challenge in data mining context. It has practical importance in a wide of variety of applications such as text, web-log and market basket data analysis. Typically, the data that arises in these applications is arranged as a two-way contingency or co-occurrence table. In this paper, we embed the block clustering problem in the mixture approach. We propose a Poisson block mixture model and adopting the classification maximum likelihood principle we perform a new algorithm. Simplicity, fast convergence and scalability are the major advantages of the proposed approach.

EPrint Type:Conference or Workshop Item (Invited Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
ID Code:1947
Deposited By:Gérard Govaert
Deposited On:30 December 2005