NDLI: A General Framework for Fast Co-clustering on Large Datasets Using Matrix Decomposition

Content Provider	IEEE Xplore Digital Library
Author	Feng Pan Xiang Zhang Wei Wang
Copyright Year	2008
Description	Author affiliation: Dept. of Comput. Sci., Univ. of North Carolina, Chapel Hill, NC (Feng Pan; Xiang Zhang; Wei Wang)
Abstract	Simultaneously clustering columns and rows (co- clustering) of large data matrix is an important problem with wide applications, such as document mining, microarray analysis, and recommendation systems. Several co-clustering algorithms have been shown effective in discovering hidden clustering structures in the data matrix. For a data matrix of m rows and n columns, the time complexity of these methods is usually in the order of mtimesn (if not higher). This limits their applicability to data matrices involving a large number of columns and rows. Moreover, an implicit assumption made by existing co-clustering methods is that the whole data matrix needs to be held in the main memory. In this paper, we propose a general framework, CRD, for co-clustering large datasets utilizing recently developed sampling- based matrix decomposition methods. The time complexity of our approach is linear in m and n. And it does not require the whole data matrix be in the main memory. Experimental results show that CRD achieves competitive accuracy to existing co-clustering methods but with much less computational cost.
Starting Page	1337
Ending Page	1339
File Size	753185
Page Count	3
File Format	PDF
ISBN	9781424418367
DOI	10.1109/ICDE.2008.4497548
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-04-07
Publisher Place	Mexico
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Matrix decomposition Clustering algorithms Data analysis Gene expression Partitioning algorithms Computer science Application software Text analysis Computational efficiency Data mining
Content Type	Text
Resource Type	Article

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