Combined constraint-based with metric-based in semi-supervised clustering ensemble

Recently, both semi-supervised clustering and cluster ensemble have received tremendous attention due to their accurate and reliable performance. There are mainly two kinds of existing semi-supervised clustering algorithms called constraint-based and metric-based. In this paper, we present a semi-supervised clustering ensemble approach which takes both pairwise constraints and metric measure into account. Firstly, under the assistance of supervised information included pairwise constraints and labeled data, the approach generates different base clustering partitions respectively using constraint-based semi-supervised clustering and metric-based semi-supervised clustering, in which the latter develops a new metric function. Given the spatial particularity of image pixels, the metric considers spatial distribution of surrounding pixels besides inherent features of pixels in the process of image feature extraction. And then the target clustering is obtained by integrating those base clustering partitions into an ensemble function. Finally, we conduct experimental verification on general data sets and image data sets, and compare clustering performance of our approach with those of other approaches. Both theoretical analysis and experimental results demonstrate that the proposed method produces considerable improvement in clustering accuracy and yields superior clustering results over a number of representative clustering methods.