Name
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. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.4018/jswis.2008070103 | INTERNATIONAL JOURNAL ON SEMANTIC WEB AND INFORMATION SYSTEMS |
Keywords | Field | DocType |
Automation, Clustering, Instance-based Learning, Pseudo-metrics | Data mining,Fuzzy clustering,CURE data clustering algorithm,Clustering high-dimensional data,Correlation clustering,Pattern recognition,Computer science,Artificial intelligence,Constrained clustering,Conceptual clustering,Cluster analysis,Medoid | Journal |
Volume | Issue | ISSN |
4 | 3 | 1552-6283 |
Citations | PageRank | References |
8 | 0.55 | 0 |
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 |