On semi-supervised clustering via multiobjective optimization

Semi-supervised classification uses aspects of both unsupervised and supervised learning to improve upon the performance of traditional classification methods. Semi-supervised clustering, in particular, explicitly integrates both information about the data distribution and about class memberships into the clustering process. In this paper, the potential of a multiobjective formulation of the semi-supervised clustering problem is explored, and two evolutionary multiobjective approaches to the problem are outlined. Experimental results demonstrate practical performance benefits of this methodology, including an improved classification performance and an increased robustness towards annotation errors. Categories and Subject Descriptors: I.5 [Pattern Recognition]: Clustering General Terms: Algorithms.

• Type: Conference
• Year: 2006
• Where: GECCO
• Authors: Julia Handl, Joshua D. Knowles
• Keywords: GECCO 2006, Optimization, Semi-supervised Classification, Semi-supervised Clustering, Semi-supervised Clustering Problem