PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Comparison Between Block CEM and Two-Way CEM Algorithm to Cluster a contingency table
Mohamed Nadif and Gérard Govaert
In: Knowledge Discovery in Databases: PKDD 2005, 9th European Conference on Principles and Practice of Knowledge Discovery in Databases}, 3-7 oct 2005, Porto, Portugal.

Abstract

When the data consists of a set of objects described by a set of variables, we have recently proposed a new mixture model which takes into account the block clustering problem on the both sets and have developed the {\it block CEM} algorithm. In this paper, we embed the block clustering problem of contingency table in the mixture approach. In using a Poisson model and adopting the classification maximum likelihood principle we perform an adapted version of block CEM. We evaluate its performance and compare it to a simple use of CEM applied on the both sets separately. We present detailed experimental results on simulated data and on real data and we show the interest of this new algorithm.

EPrint Type:Conference or Workshop Item (Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
ID Code:1952
Deposited By:Gérard Govaert
Deposited On:30 December 2005