Name
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. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-68234-9_25 | ESWC |
Keywords | Field | DocType |
features committee,novelty detection,conceptual clustering,final experiment,concept description,absolute quality index,concept drift,clustering method,clustering algorithm,partitional method,concept formation,semi-distance measure | Canopy clustering algorithm,Fuzzy clustering,Data mining,Correlation clustering,Computer science,Concept drift,Artificial intelligence,Constrained clustering,Conceptual clustering,Cluster analysis,Machine learning,Medoid | Conference |
Volume | ISSN | ISBN |
5021 | 0302-9743 | 3-540-68233-3 |
Citations | PageRank | References |
22 | 0.96 | 24 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nicola Fanizzi | 1 | 1124 | 90.54 |
| Claudia D'Amato | 2 | 733 | 57.03 |
| Floriana Esposito | 3 | 2434 | 277.96 |