Machine Learning
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Information-theoretic metric learning
Proceedings of the 24th international conference on Machine learning
Learning nonparametric kernel matrices from pairwise constraints
Proceedings of the 24th international conference on Machine learning
Enhancing semi-supervised clustering: a feature projection perspective
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Semi-Supervised Learning
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Constrained Clustering is a data mining technique that produces clusters of similar data by using pre-given constraints about data pairs. If we consider using constrained clustering for some practical interactive systems such as information retrieval or recommendation systems, the cost of constraint preparation will be the problem as well as other machine learning techniques. In this paper, we propose a method to complement the lack of constraints by using co-training framework, which extends training examples by leveraging two kinds of feature sets. Our method is based on a constrained clustering ensemble algorithm that integrates a set of clusters produced by a constrained k-means with random ordered data assignment, and runs the same algorithm on two different feature sets to extend constraints. We evaluate our method on a Web page dataset that provides two different feature sets. The results show that our method achieves the performance improvement by using co-training approach.