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