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Issue No.04  April (2013 vol.25)
pp: 932944
Xiaojun Chen , Shenzhen Grad. Sch., Harbin Inst. of Technol., Shenzhen, China
Xiaofei Xu , Dept. of Comput. Sci. & Eng., Harbin Inst. of Technol., Harbin, China
J. Z. Huang , Coll. of Comput. Sci. & Software, Shenzhen Univ., Shenzhen, China
Yunming Ye , Shenzhen Grad. Sch., Harbin Inst. of Technol., Shenzhen, China
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2011.262
ABSTRACT
This paper proposes TWkmeans, an automated twolevel 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 kmeans clustering process to automatically compute the view weights and the variable weights. We used two reallife data sets to investigate the properties of two types of weights in TWkmeans and investigated the difference between the weights of TWkmeans and the weights of the individual variable weighting method. The experiments have revealed the convergence property of the view weights in TWkmeans. We compared TWkmeans with five clustering algorithms on three reallife data sets and the results have shown that the TWkmeans algorithm significantly outperformed the other five clustering algorithms in four evaluation indices.
INDEX TERMS
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
CITATION
Xiaojun Chen, Xiaofei Xu, J. Z. Huang, Yunming Ye, "TWkmeans: Automated twolevel variable weighting clustering algorithm for multiview data", IEEE Transactions on Knowledge & Data Engineering, vol.25, no. 4, pp. 932944, April 2013, doi:10.1109/TKDE.2011.262
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