NDLI: A Novel Co-clustering Method with Intra-similarities

Content Provider	IEEE Xplore Digital Library
Author	Jian-Sheng Wu Jian-Huang Lai Chang-Dong Wang
Copyright Year	2011
Abstract	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.
Starting Page	300
Ending Page	306
File Size	192312
Page Count	7
File Format	PDF
ISBN	9781467300056
DOI	10.1109/ICDMW.2011.15
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-11
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Decision support systems Algorithm design and analysis document similarities co-clustering Clustering algorithms Prototypes word similarities Partitioning algorithms Joints Mutual information
Content Type	Text
Resource Type	Article

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