Fast evolutionary maximum margin clustering

The maximum margin clustering approach is a recently proposed extension of the concept of support vector machines to the clustering problem. Briefly stated, it aims at finding an optimal partition of the data into two classes such that the margin induced by a subsequent application of a support vector machine is maximal. We propose a method based on stochastic search to address this hard optimization problem. While a direct implementation would be infeasible for large data sets, we present an efficient computational shortcut for assessing the “quality” of intermediate solutions. Experimental results show that our approach outperforms existing methods in terms of clustering accuracy.

• Type: Conference
• Year: 2009
• Where: ICML
• Authors: Fabian Gieseke, Tapio Pahikkala, Oliver Kramer
• Keywords: Clustering Problem, ICML 2009, Machine Learning, Margin Clustering Approach, Support Vector Machine