NDLI: TW-k-means: Automated two-level variable weighting clustering algorithm for multiview data

Content Provider	IEEE Xplore Digital Library
Author	Xiaojun Chen Xiaofei Xu Huang, J.Z. Yunming Ye
Copyright Year	1989
Abstract	This paper proposes TW-k-means, an automated two-level variable weighting clustering algorithm for multiview data, which can simultaneously compute weights for views and individual variables. In this algorithm, a view weight is assigned to each view to identify the compactness of the view and a variable weight is also assigned to each variable in the view to identify the importance of the variable. Both view weights and variable weights are used in the distance function to determine the clusters of objects. In the new algorithm, two additional steps are added to the iterative k-means clustering process to automatically compute the view weights and the variable weights. We used two real-life data sets to investigate the properties of two types of weights in TW-k-means and investigated the difference between the weights of TW-k-means and the weights of the individual variable weighting method. The experiments have revealed the convergence property of the view weights in TW-k-means. We compared TW-k-means with five clustering algorithms on three real-life data sets and the results have shown that the TW-k-means algorithm significantly outperformed the other five clustering algorithms in four evaluation indices.
Sponsorship	IEEE IEEE Comput. Soc. Tech. Committee on Data Eng IEEE Computer Society
Starting Page	932
Ending Page	944
Page Count	13
File Size	1529670
File Format	PDF
ISSN	10414347
Volume Number	25
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Clustering algorithms Partitioning algorithms Computational modeling Clustering methods Web pages Data models Algorithm design and analysis variable weighting Data mining clustering multiview learning $(k)$-means
Content Type	Text
Resource Type	Article
Subject	Information Systems Computational Theory and Mathematics Computer Science Applications

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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