Block clustering of contingency table and mixture model. (English) Zbl 1165.68418

Nadif, Mohamed; Govaert, Gérard

Fazel Famili, A. (ed.) et al., Advances in intelligent data analysis VI. 6th international symposium on intelligent data analysis, IDA 2005, Madrid, Spain, September 8–10, 2005. Proceedings. Berlin: Springer (ISBN 3-540-28795-7/pbk). Lecture Notes in Computer Science 3646, 249-259 (2005).

Summary: 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.
For the entire collection see [Zbl 1084.68005].

MSC:

68T05	Learning and adaptive systems in artificial intelligence
62H17	Contingency tables