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Kernel-based MinMax clustering methods with kernelization of the metric and auto-tuning hyper-parameters
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DOI：10.1016/j.neucom.2019.05.056.png)
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Abstract
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This paper proposes kernel-based MinMax clustering methods with kernelization of the metric and autotuning hyper-parameters which learn the variable weights and adjust the cluster weights automatically. We develop the new objective functions that are obtained from the proposed algorithms to achieve the desirable partition by minimizing the dissimilarity measures with kernelization of the metric. Correspondingly, two additional steps are introduced to k-means algorithms, so that, not only the performance is improved, but also the efficiency remains. More specifically, the proposed algorithms learn two types of weights at each iteration where variable weights identify relevant variables and cluster weights to confine the occurrence of the large variance cluster. Finally, the experiments on ten UCI benchmark datasets corroborate the superiority of the proposed algorithms. (C) 2019 Elsevier B.V. All rights reserved.
Keywords:
Kernel clustering
Kernelization of the metric
Auto-tuning hyper-parameters
MinMax optimization
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