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Clustering in large data sets with the limited memory bundle method
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DOI：10.1016/j.patcog.2018.05.028.png)
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Abstract
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The aim of this paper is to design an algorithm based on nonsmooth optimization techniques to solve the minimum sum-of-squares clustering problems in very large data sets. First, the clustering problem is formulated as a nonsmooth optimization problem. Then the limited memory bundle method [Haarala et al., 2007] is modified and combined with an incremental approach to design a new clustering algorithm. The algorithm is evaluated using real world data sets with both the large number of attributes and the large number of data points. It is also compared with some other optimization based clustering algorithms. The numerical results demonstrate the efficiency of the proposed algorithm for clustering in very large data sets. (C) 2018 Elsevier Ltd. All rights reserved.
Keywords:
Cluster analysis
Nonsmooth optimization
Nonconvex optimization
Bundle methods
Limited memory methods
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