NDLI: CRD: fast co-clustering on large datasets utilizing sampling-based matrix decomposition

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CRD: fast co-clustering on large datasets utilizing sampling-based matrix decomposition

Content Provider	ACM Digital Library
Author	Wang, Wei Pan, Feng Zhang, Xiang
Abstract	The problem of simultaneously clustering columns and rows (co-clustering) arises in important applications, such as text data mining, microarray analysis, and recommendation system analysis. Compared with the classical clustering algorithms, co-clustering algorithms have been shown to be more effective in discovering hidden clustering structures in the data matrix. The complexity of previous co-clustering algorithms is usually O(m X n), where m and n are the numbers of rows and columns in the data matrix respectively. This limits their applicability to data matrices involving a large number of columns and rows. Moreover, some huge datasets can not be entirely held in main memory during co-clustering which violates the assumption made by the previous algorithms. In this paper, we propose a general framework for fast co-clustering large datasets, CRD. By utilizing recently developed sampling-based matrix decomposition methods, CRD achieves an execution time linear in m and n. Also, CRD does not require the whole data matrix be in the main memory. We conducted extensive experiments on both real and synthetic data. Compared with previous co-clustering algorithms, CRD achieves competitive accuracy but with much less computational cost.
Starting Page	173
Ending Page	184
Page Count	12
File Format	PDF
ISBN	9781605581026
DOI	10.1145/1376616.1376637
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2008-06-09
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Matrix decomposition Co-clustering
Content Type	Text
Resource Type	Article

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