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. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Nadif, M., & Govaert, G. (2005). Block clustering of contingency table and mixture model. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3646 LNCS, pp. 249–259). Springer Verlag. https://doi.org/10.1007/11552253_23
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