A simulated annealing‐based maximum‐margin clustering algorithm

Maximum-margin clustering (MMC) is an extension of the support vector machine (SVM) to clustering. It partitions a set of unlabelled 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 non-convexity of the optimization problem. In this paper, we propose a simulated annealing-based algorithm that is able to address the issue of local minima in the MMC problem. The novelty of our algorithm is twofold: (1) it comprises a comprehensive cluster modification scheme based on simulated annealing, and (2) 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 datasets (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.