A novel grammar-based genetic programming approach to clustering

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{conf/sac/FalcoTCG05a,

• title = "A novel grammar-based genetic programming approach to clustering",
• author = "Ivan {De Falco} and Ernesto Tarantino and Antonio {Della Cioppa} and F. Gagliardi",
• year = "2005",
• bibdate = "2006-02-10",
• pages = "928--932",
• editor = "Hisham Haddad and Lorie M. Liebrock and Andrea Omicini and Roger L. Wainwright",
• booktitle = "Proceedings of the 2005 ACM Symposium on Applied Computing (SAC)",
• publisher = "ACM",
• address = "Santa Fe, New Mexico, USA",
• month = mar # " 13-17",
• organisation = "ACM",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/sac/sac2005.html#FalcoTCG05a",
• keywords = "genetic algorithms, genetic programming, Information Storage and Retrieval, Information search and retrieval, clustering, retrieval methods, Artificial Intelligence, Problem Solving, Control Methods, and Search heuristic methods, Algorithms, Experimentation, data clustering, EM, Expectation-Maximisation",
• ISBN = "1-58113-964-0",
• DOI = "doi:10.1145/1066677.1066891",
• abstract = "Most of the classical methods for clustering analysis require the user setting of number of clusters. To surmount this problem, in this paper a grammar-based Genetic Programming approach to automatic data clustering is presented. An innovative clustering process is conceived strictly linked to a novel cluster representation which provides intelligible information on patterns. The efficacy of the implemented partitioning system is estimated on a medical domain by exploiting expressly defined evaluation indices. Furthermore, a comparison with other clustering tools is performed.",

}

Genetic Programming entries for Ivanoe De Falco Ernesto Tarantino Antonio Della Cioppa F Gagliardi

Citations