Evolutionary Conceptual Clustering Based on Induced Pseudo-Metrics

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@Article{DBLP:journals/ijswis/FanizzidE08,

• author = "Nicola Fanizzi and Claudia d'Amato and Floriana Esposito",
• title = "Evolutionary Conceptual Clustering Based on Induced Pseudo-Metrics",
• journal = "International Journal on Semantic Web and Information Systems",
• year = "2008",
• volume = "4",
• number = "3",
• pages = "44--67",
• keywords = "genetic algorithms, genetic programming",
• URL = "https://doi.org/10.4018/jswis.2008070103",
• DOI = "doi:10.4018/jswis.2008070103",
• timestamp = "Tue, 06 Jun 2017 22:21:42 +0200",
• biburl = "https://dblp.org/rec/bib/journals/ijswis/FanizzidE08",
• bibsource = "dblp computer science bibliography, https://dblp.org",
• 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.",
• notes = "IJSWIS",

}

Genetic Programming entries for Nicola Fanizzi Claudia d'Amato Floriana Esposito

Citations