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Optimized Data Fusion for Kernel k-Means Clustering

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7 Author(s)
Shi Yu ; Dept. of Med., Univ. of Chicago, Chicago, IL, USA ; Tranchevent, L.-C. ; Xinhai Liu ; Glanzel, W.
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This paper presents a novel optimized kernel k-means algorithm (OKKC) to combine multiple data sources for clustering analysis. The algorithm uses an alternating minimization framework to optimize the cluster membership and kernel coefficients as a nonconvex problem. In the proposed algorithm, the problem to optimize the cluster membership and the problem to optimize the kernel coefficients are all based on the same Rayleigh quotient objective; therefore the proposed algorithm converges locally. OKKC has a simpler procedure and lower complexity than other algorithms proposed in the literature. Simulated and real-life data fusion applications are experimentally studied, and the results validate that the proposed algorithm has comparable performance, moreover, it is more efficient on large-scale data sets. (The Matlab implementation of OKKC algorithm is downloadable from

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Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:34 ,  Issue: 5 )