Evolutionary Conceptual Clustering Based on Induced Pseudo-Metrics

Nicola Fanizzi, Claudia d’Amato and Floriana Esposito
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Nicola Fanizzi: Universita degli studi di Bari, Italy
Claudia d’Amato: Universita degli studi di Bari, Italy
Floriana Esposito: Universita degli studi di Bari, Italy

International Journal on Semantic Web and Information Systems (IJSWIS), 2008, vol. 4, issue 3, 44-67

Abstract: We present a method based on clustering techniques to detect possible/probable novel concepts or concept drift in a Description Logics knowledge base. The method exploits a semi-distance measure defined for individuals, that is based on a finite number of dimensions corresponding to a committee of discriminating features (concept descriptions). A maximally discriminating group of features is obtained with a randomized optimization method. In the algorithm, the possible clusterings are represented as medoids (w.r.t. the given metric) of variable length. The number of clusters is not required as a parameter, the method is able to find an optimal choice by means of evolutionary operators and a proper fitness function. An experimentation proves the feasibility of our method and its effectiveness in terms of clustering validity indices. With a supervised learning phase, each cluster can be assigned with a refined or newly constructed intensional definition expressed in the adopted language.

Date: 2008
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