Conceptual Clustering and Its Application to Concept Drift and Novelty Detection

Abstract. The paper presents a clustering method which can be applied to populated ontologies for discovering interesting groupings of resources therein. The method exploits a simple, yet effective and language-independent, semi-distance measure for individuals, that is based on their underlying semantics along with a number of dimensions corresponding to a set of concept descriptions (discriminating features committee). The clustering algorithm is a partitional method and it is based on the notion of medoids w.r.t. the adopted semi-distance measure. Eventually, it produces a hierarchical organization of groups of individuals. A final experiment demonstrates the validity of the approach using absolute quality indices. We propose two possible exploitations of these clusterings: concept formation and detecting concept drift or novelty.

• Type: Conference
• Year: 2008
• Where: ESWS
• Authors: Nicola Fanizzi, Claudia d'Amato, Floriana Esposito
• Keywords: Clustering Method, ESWS 2008, Internet Technology, Populated Ontologies, Semi-distance Measure