MMMs-Induced Fuzzy Co-clustering with Exclusive Partition Penalty on Selected Items

Fuzzy co-clustering is a powerful tool for summarizing co-occurrence information while some intrinsic knowledge on meaningful items may be concealed by the dominant items shared by multiple clusters. In this paper, the conventional fully exclusive item partition model is modified such that exclusive penalties are forced only on some selected items. Its advantages are demonstrated through two numerical experiments. In a document clustering task, the proposed model is utilized for emphasizing cluster-wise meaningful keywords, which are useful for effectively summarizing document clusters. In an unsupervised classification task, the classification quality is improved by efficiently selecting promising items based on the item-wise single penalization test.