Maximum Margin Clustering with Multivariate Loss Function

• Published in: 2009 Ninth IEEE International Conference on Data Mining pp. 637 - 646
• Main Authors: Bin Zhao, Kwok, J., Changshui Zhang
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2009
• Subjects: Clustering algorithms; Data mining; Error analysis; Labeling; Laboratories; Loss measurement; Machine learning algorithms; maximum margin clustering; multivariate performance measure; Mutual information; Performance loss; Support vector machines
• ISBN: 9781424452422; 1424452422
• ISSN: 1550-4786; 2374-8486
• DOI: 10.1109/ICDM.2009.37

This paper presents a simple but powerful extension of the maximum margin clustering (MMC) algorithm that optimizes multivariate performance measure specifically defined for clustering, including normalized mutual information, rand index and F-measure. Different from previous MMC algorithms that always employ the error rate as the loss function, our formulation involves a multivariate loss function that is a non-linear combination of the individual clustering results. Computationally, we propose a cutting plane algorithm to approximately solve the resulting optimization problem with a guaranteed accuracy. Experimental evaluations show clear improvements in clustering performance of our method over previous maximum margin clustering algorithms.