CECM: Adding pairwise constraints to evidential clustering

Fuzzy or hard partitioning methods aim at grouping objects according to their similarity. Recently, a new concept of partition based on belief function theory, called credal partition, has been proposed and has been shown to generate meaningful description of the data. Hard, fuzzy or credal partitions are generally obtained using unsupervised learning methods, using only the numeric description between two objects to compute their similarity. However, in some applications, some kind of background knowledge about the objects or about the clusters is available. To integrate this auxiliary information, constraint-based (or semi-supervised) methods have been proposed. A popular type of constraints specifies whether two objects are in the same cluster (must-link) or in different clusters (cannot-link). We propose here a new algorithm, called CECM, which computes a credal partition using a constrained clustering method. We show how to translate the available information into constraints, and how to integrate them in the search of the credal partition. The paper ends with some experimental results. Results of CECM are compared to other constrained clustering algorithms. Then an application in image segmentation is described.