Genetic-guided semi-supervised clustering algorithm with instance-level constraints

Authors:
Yi Hong;Sam Kwong;Hui Xiong;Qingsheng Ren
Affiliations:
City University of Hong Kong, Hong Kong;City University of Hong Kong, Hong Kong;Rutgers University, NJ, USA;Shanghai Jiao Tong University, Shanghai, China
Venue:
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Year:
2008

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Abstract

Semi-supervised clustering with instance-level constraints is one of the most active research topics in the areas of pattern recognition, machine learning and data mining. Several recent studies have shown that instance-level constraints can significantly increase accuracies of a variety of clustering algorithms. However, instance-level constraints may split the search space of the optimal clustering solution into pieces, thus significantly compound the difficulty of the search task. This paper explores a genetic approach to solve the problem of semi-supervised clustering with instance-level constraints. In particular, a novel semi-supervised clustering algorithm with instance-level constraints, termed as the hybrid genetic-guided semi-supervised clustering algorithm with instance-level constraints (Cop-HGA), is proposed. Cop-HGA uses a hybrid genetic algorithm to perform the search task of a high quality clustering solution that is able to draw a good balance between predefined clustering criterion and available instance-level background knowledge. The effectiveness of Cop-HGA is confirmed by experimental results on several real data sets with artificial instance-level constraints.