Multiple Kernel Clustering

Abstract

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2009 SIAM International Conference on Data Mining (SDM)Multiple Kernel ClusteringBin Zhao, James T. Kwok, and Changshui ZhangBin Zhao, James T. Kwok, and Changshui Zhangpp.638 - 649Chapter DOI:https://doi.org/10.1137/1.9781611972795.55PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract Maximum margin clustering (MMC) has recently attracted considerable interests in both the data mining and machine learning communities. It first projects data samples to a kernel-induced feature space and then performs clustering by finding the maximum margin hyperplane over all possible cluster labelings. As in other kernel methods, choosing a suitable kernel function is imperative to the success of maximum margin clustering. In this paper, we propose a multiple kernel clustering (MKC) algorithm that simultaneously finds the maximum margin hyperplane, the best cluster labeling, and the optimal kernel. Moreover, we provide detailed analysis on the time complexity of the MKC algorithm and also extend multiple kernel clustering to the multi-class scenario. Experimental results on both toy and real-world data sets demonstrate the effectiveness and efficiency of the MKC algorithm. Previous chapter Next chapter RelatedDetails Published:2009ISBN:978-0-89871-682-5eISBN:978-1-61197-279-5 https://doi.org/10.1137/1.9781611972795Book Series Name:ProceedingsBook Code:PR133Book Pages:1-1244