Simultaneous clustering and outlier detection using dominant sets

Eyasu Zemene, Yonatan Tariku Tesfaye, Andrea Prati, Marcello Pelillo

Abstract: We present a unified approach for simultaneous clustering and outlier detection in data. We utilize some properties of a family of quadratic optimization problems related to dominant sets, a well-known graph-theoretic notion of a cluster which generalizes the concept of a maximal clique to edge-weighted graphs. Unlike most (all) of the previous techniques, in our framework the number of clusters arises intuitively and outliers are obliterated automatically. The resulting algorithm discovers both parameters from the data. Experiments on real and on large scale synthetic dataset demonstrate the effectiveness of our approach and the utility of carrying out both clustering and outlier detection in a concurrent manner.