Evolutionary Conceptual Clustering Based on Induced Pseudo-Metrics

Evolutionary Conceptual Clustering Based on Induced Pseudo-Metrics

Nicola Fanizzi (Università degli studi di Bari, Italy), Claudia d’Amato (Università degli studi di Bari, Italy) and Floriana Esposito (Università degli Studi di Bari, Italy)
DOI: 10.4018/978-1-60566-992-2.ch012
OnDemand PDF Download:
$30.00
List Price: $37.50

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.

Complete Chapter List

Search this Book:
Reset