首页|A simulated annealing-based maximum-margin clustering algorithm

A simulated annealing-based maximum-margin clustering algorithm

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Maximum-margin clustering is an extension of the support vector machine (SVM) to clustering. It partitions a set of unlabeled data into multiple groups by finding hyperplanes with the largest margins. Although existing algorithms have shown promising results, there is no guarantee of convergence of these algorithms to global solutions due to the nonconvexity of the optimization problem. In this paper, we propose a simulated annealing-based algorithm that is able to mitigate the issue of local minima in the maximum-margin clustering problem. The novelty of our algorithm is twofold, ie, (i) it comprises a comprehensive cluster modification scheme based on simulated annealing, and (ii) it introduces a new approach based on the combination of k-means++ and SVM at each step of the annealing process. More precisely, k-means++ is initially applied to extract subsets of the data points. Then, an unsupervised SVM is applied to improve the clustering results. Experimental results on various benchmark data sets (of up to over a million points) give evidence that the proposed algorithm is more effective at solving the clustering problem than a number of popular clustering algorithms.

k-means plus plus algorithmmaximum margin clusteringsimulated annealing

Seifollahi, Sattar、Bagirov, Adil、Borzeshi, Ehsan Zare、Piccardi, Massimo

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Univ Technol Sydney, Fac Engn & Informat Technol, Sydney, NSW, Australia|CMCRC, Sydney, NSW, Australia

Federat Univ Australia, Fac Sci & Technol, Ballarat, Vic, Australia

CMCRC, Sydney, NSW, Australia

Univ Technol Sydney, Fac Engn & Informat Technol, Sydney, NSW, Australia

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2019

10.1111/coin.l2187

Computational Intelligence

Computational Intelligence

EISCI
ISSN：0824-7935
年,卷(期)：2019.35(1)