]> BibSonomy publications for/author/Esposito Computer Physics Communications12956-971PArthENoPE: Public algorithm evaluating the nucleosynthesis of primordial elements.1782008dblp 2008-06-24 00:00:00.0 PARAconf/para/1996239-246Lecture Notes in Computer ScienceParallel Heuristics for Bandwidth Reduction of Sparse Matrices with IBM SP2 and Cray T3D.11841996dblp 2008-06-23 00:00:00.0 UICconf/uic/2008119-130Lecture Notes in Computer ScienceA Framework for Context-Aware Home-Health Monitoring.50612008dblp 2008-06-23 00:00:00.0 Oper. Res. Lett.3-599-107A new matrix bandwidth reduction algorithm.231998dblp 2008-06-23 00:00:00.0 SWAPconf/swap/2007CEUR Workshop ProceedingsBuilding Rules on top of Ontologies? Inductive Logic Programming can help!3142007dblp 2008-06-09 00:00:00.0 SWAPconf/swap/2007CEUR Workshop ProceedingsSemantic Nearest Neighbor Search in OWL Ontologies.3142007dblp 2008-06-09 00:00:00.0 URSWconf/semweb/2007urswCEUR Workshop ProceedingsApproximate Measures of Semantic Dissimilarity under Uncertainty.3272007dblp 2008-06-06 00:00:00.0 SMRRconf/semweb/2007smrrCEUR Workshop ProceedingsEfficient Discovery of Services Specified in Description Logics Languages.2432007dblp 2008-06-04 00:00:00.0 Description Logicsconf/dlog/2007CEUR Workshop ProceedingsInduction of Optimal Semi-distances for Individuals based on Feature Sets.2502007dblp 2008-05-29 00:00:00.0 Berlin, HeidelbergProceedings of the 5th European Semantic Web ConferenceJuneLNCSConceptual Clustering and its Application to Concept Drift and Novelty Detection2008clustering concept conceptual drift learning novelty semantic setection similarity 2008-05-28 14:50:02.0We present a method based on clustering techniques to detect concept drift or novelty in a knowledge based expressed in Description Logics. The method exploits an effective and language-independent semi-distance measure defined for the space of individuals, that is based on a finite number of dimensions corresponding to a committee of discriminating features (represented by concept descriptions). A maximally discriminating group of features can be obtained with the randomized optimization methods described in the paper. An experimentation with some ontologies proves the feasibility of our method and its effectiveness in terms of clustering validity indices. Then, with a supervised learning phase, each cluster can be assigned with a refined or newly constructed intensional definition expressed in the adopted language. We propose a method for exploiting the clustering results for concept drift and novelty detection