%0 Conference Paper %1 conf/sac/FalcoTCG05a %A De Falco, Ivan %A Tarantino, Ernesto %A Della Cioppa, Antonio %A Gagliardi, F. %B Proceedings of the 2005 ACM Symposium on Applied Computing (SAC) %C Santa Fe, New Mexico, USA %D 2005 %E Haddad, Hisham %E Liebrock, Lorie M. %E Omicini, Andrea %E Wainwright, Roger L. %I ACM %K Algorithms, Artificial Control EM, Expectation-Maximisation Experimentation, Information Intelligence, Methods, Problem Retrieval, Search Solving, Storage algorithms, and clustering, data genetic heuristic methods, programming, retrieval retrieval, search %P 928--932 %T A novel grammar-based genetic programming approach to clustering %U http://doi.acm.org/10.1145/1066677.1066891 %X 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. %@ 1-58113-964-0

@inproceedings{conf/sac/FalcoTCG05a, 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.}, added-at = {2008-06-19T17:35:00.000+0200}, address = {Santa Fe, New Mexico, USA}, author = {{De Falco}, Ivan and Tarantino, Ernesto and {Della Cioppa}, Antonio and Gagliardi, F.}, bibdate = {2006-02-10}, bibsource = {DBLP, http://dblp.uni-trier.de/db/conf/sac/sac2005.html#FalcoTCG05a}, biburl = {https://www.bibsonomy.org/bibtex/2ef7df5ad05c3f7c1cdd268fcab12b679/brazovayeye}, booktitle = {Proceedings of the 2005 ACM Symposium on Applied Computing (SAC)}, editor = {Haddad, Hisham and Liebrock, Lorie M. and Omicini, Andrea and Wainwright, Roger L.}, interhash = {f5023c327959024a488b781e5b1fe251}, intrahash = {ef7df5ad05c3f7c1cdd268fcab12b679}, isbn = {1-58113-964-0}, keywords = {Algorithms, Artificial Control EM, Expectation-Maximisation Experimentation, Information Intelligence, Methods, Problem Retrieval, Search Solving, Storage algorithms, and clustering, data genetic heuristic methods, programming, retrieval retrieval, search}, month = {March 13-17}, organisation = {ACM}, pages = {928--932}, publisher = {ACM}, timestamp = {2008-06-19T17:38:29.000+0200}, title = {A novel grammar-based genetic programming approach to clustering}, url = {http://doi.acm.org/10.1145/1066677.1066891}, year = 2005 }