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2008
Springer

Conceptual Clustering and Its Application to Concept Drift and Novelty Detection

10 years 3 months ago
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.
Nicola Fanizzi, Claudia d'Amato, Floriana Esposito
Added 19 Oct 2010
Updated 19 Oct 2010
Type Conference
Year 2008
Where ESWS
Authors Nicola Fanizzi, Claudia d'Amato, Floriana Esposito
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