Semi-supervised Clustering and Aggregation of Relational Data

We introduce a new semi-supervised approach for clustering and aggregating relational data (SS-CARD). We assume that data is available in a relational form, where we only have information about the degrees to which pairs of objects in the data are related. Moreover, we assume that the relational information is represented by multiple dissimilarity matrices. These matrices could have been generated using different sensors, features, or mappings. The SS-CARD uses partial supervision information that consists of a small set of must-link and cannot-link constraints. The performance of the proposed algorithm is illustrated by using it to categorize a collection of 500 color images. The results are compared with those obtained by 3 other relational clustering methods.