Partial closure-based constrained clustering with order ranking

In this paper we propose a new partial closure-based constrained clustering algorithm. We introduce closures into the partial constrained clustering and we propose a new measurement to order the importance of the constrained closures. Experiments on public datasets demonstrate the advantages of our algorithm over the standard Kmeans and two state-of-the-art constrained clustering algorithms.

• Type: Conference
• Year: 2008
• Where: ICPR
• Authors: Shaohong Zhang, Hau-San Wong
• Keywords: Closure-based Constrained Clustering, Computer Vision, Constrained Clustering, Constrained Clustering Algorithms, ICPR 2008