GROUP OUTLIER FACTOR: A NEW SCORE USING SELF-ORGANISING MAP FOR GROUP-OUTLIER AND NOVELTY DETECTION

Abstract

This paper describe a new concept of "cluster outlier-ness". In order to quantify it, we propose a relative isolation score named group outlier factor (GOF). GOF is a score, which is computed during a clustering process using self-organizing maps. The main difference between GOF and existing methods is that, being an outlier is not associated to a single pattern but to a cluster. Thus, an outlier factor (OF) with respect to each cluster is computed for each new sample and compared to the GOF score associated for each cluster. OF is used as a novelty detection classifier. This approach allows to identify meaningful outlier-clusters and detects novel patterns that previous approaches could not find. Experimental results and comparison studies show that the use of GOF sensibly improves the results in term of cluster-outlier and novelty detection.