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2009
SIAM

Integrated KL (K-means - Laplacian) Clustering: A New Clustering Approach by Combining Attribute Data and Pairwise Relations.

10 years 4 months ago
Integrated KL (K-means - Laplacian) Clustering: A New Clustering Approach by Combining Attribute Data and Pairwise Relations.
Most datasets in real applications come in from multiple sources. As a result, we often have attributes information about data objects and various pairwise relations (similarity) between data objects. Traditional clustering algorithms use either data attributes only or pairwise similarity only. We propose to combine K-means clustering on data attributes and normalized cut spectral clustering on pairwise relations. We show that these two methods can be coherently integrated together to make use of different data sources to obtain good clustering results. We also show that our integrated KL (K-means - Laplacian) clustering method can be naturally extended to semi-supervised clustering, data embedding and metric learning. Finally the experimental results on benchmark data sets are presented to show the effectiveness of our method.
Fei Wang, Chris H. Q. Ding, Tao Li
Added 07 Mar 2010
Updated 07 Mar 2010
Type Conference
Year 2009
Where SDM
Authors Fei Wang, Chris H. Q. Ding, Tao Li
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