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Optimization based DC programming and DCA for hierarchical clustering
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DOI：10.1016/j.ejor.2005.07.028.png)
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Abstract
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One of the most promising approaches for clustering is based on methods of mathematical programming. In this paper we propose new optimization methods based on DC (Difference of Convex functions) programming for hierarchical clustering. A bilevel hierarchical clustering model is considered with different optimization formulations. They are all nonconvex, nonsmooth optimization problems for which we investigate attractive DC optimization Algorithms called DCA. Numerical results on some artificial and real-world databases are reported. The results demonstrate that the proposed algorithms are more efficient than related existing methods. (C) 2006 Elsevier B.V. All rights reserved.
Keywords:
clustering
multilevel hierarchical clustering
K-means algorithm
nonsmooth nonconvex programs
DC programming
DCA
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