Semi-supervised Variable Weighting for Clustering

Semi-supervised learning, which uses a small amount of labeled data in conjunction with a large amount of unlabeled data for training, has recently attracted huge research attention due to the considerable improvement in learning accuracy. In this work, we focus on semisupervised variable weighting for clustering, which is a critical step in clustering as it is known that interesting clustering structure usually occurs in a subspace defined by a subset of variables. Besides exploiting both labeled and unlabeled data to effectively identify the real importance of variables, our method embeds variable weighting in the process of semi-supervised clustering, rather than calculating variable weights separately, to ensure the computation efficiency. Our experiments carried out on both synthetic and real data demonstrate that semisupervised variable weighting significantly improves the clustering accuracy of existing semi-supervised k -means without variable weighting, or with unsupervised variable weighting.