A Novel Co-clustering Method with Intra-similarities
Jian-Sheng Wu, Jianhuang Lai, Chang‐Dong Wang · 2011
Recently, co-clustering has become a topic of much interest because of its applications to many problems. It has been proved more effective than one-way clustering methods. But the existing co-clustering approaches just treat the document as a collection of words, disregarding the word sequences. They only consider the co-occurrence counts of words and documents, but do not take into account the similarities between words and similarities between documents. However, these similarity information can help improving the co-clustering. In this paper, we incorporate the word similarities and document similarities into the co-clustering algorithm, and propose a new co-clustering method. And we provide a theoretical analysis that our algorithm can converge to a local minimum. The empirical evaluation on publicly available data sets also shows that our algorithm is effective.