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

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].


68T05 Learning and adaptive systems in artificial intelligence
62H17 Contingency tables
Full Text: DOI