{"id":"https://openalex.org/W2154185929","doi":"https://doi.org/10.1109/cimca.2005.1631337","title":"A New Evolutionary Algorithm for Determining the Optimal Number of Clusters","display_name":"A New Evolutionary Algorithm for Determining the Optimal Number of Clusters","publication_year":2005,"publication_date":"2005-01-01","ids":{"openalex":"https://openalex.org/W2154185929","doi":"https://doi.org/10.1109/cimca.2005.1631337","mag":"2154185929"},"language":"en","primary_location":{"id":"doi:10.1109/cimca.2005.1631337","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cimca.2005.1631337","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5039346080","display_name":"Wei Lu","orcid":"https://orcid.org/0000-0002-4150-8674"},"institutions":[{"id":"https://openalex.org/I212119943","display_name":"University of Victoria","ror":"https://ror.org/04s5mat29","country_code":"CA","type":"education","lineage":["https://openalex.org/I212119943"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Wei Lu","raw_affiliation_strings":["University of Victoria, Canada","Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Victoria, Canada","institution_ids":["https://openalex.org/I212119943"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC, Canada","institution_ids":["https://openalex.org/I212119943"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041277719","display_name":"Issa Traor\u00e9","orcid":"https://orcid.org/0000-0003-2987-8047"},"institutions":[{"id":"https://openalex.org/I212119943","display_name":"University of Victoria","ror":"https://ror.org/04s5mat29","country_code":"CA","type":"education","lineage":["https://openalex.org/I212119943"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"I. Traore","raw_affiliation_strings":["Dept. of Electr. & Comput. Eng., Victoria Univ., BC","Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Electr. & Comput. Eng., Victoria Univ., BC","institution_ids":["https://openalex.org/I212119943"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC, Canada","institution_ids":["https://openalex.org/I212119943"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I212119943"],"apc_list":null,"apc_paid":null,"fwci":0.2465,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.55388368,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"648","last_page":"653"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.998199999332428,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9869999885559082,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/evolutionary-algorithm","display_name":"Evolutionary algorithm","score":0.6764858961105347},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6689721345901489},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6512686610221863},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6352118253707886},{"id":"https://openalex.org/keywords/determining-the-number-of-clusters-in-a-data-set","display_name":"Determining the number of clusters in a data set","score":0.5684995651245117},{"id":"https://openalex.org/keywords/evolutionary-computation","display_name":"Evolutionary computation","score":0.5025701522827148},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.4881513714790344},{"id":"https://openalex.org/keywords/fitness-function","display_name":"Fitness function","score":0.447751522064209},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4439956843852997},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.4403392970561981},{"id":"https://openalex.org/keywords/cluster","display_name":"Cluster (spacecraft)","score":0.4339950680732727},{"id":"https://openalex.org/keywords/genetic-algorithm","display_name":"Genetic algorithm","score":0.4124661386013031},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.40271803736686707},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3601382374763489},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3354029357433319},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3116919994354248},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.27894914150238037},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2687496840953827},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.20376724004745483},{"id":"https://openalex.org/keywords/canopy-clustering-algorithm","display_name":"Canopy clustering algorithm","score":0.17168983817100525}],"concepts":[{"id":"https://openalex.org/C159149176","wikidata":"https://www.wikidata.org/wiki/Q14489129","display_name":"Evolutionary algorithm","level":2,"score":0.6764858961105347},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6689721345901489},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6512686610221863},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6352118253707886},{"id":"https://openalex.org/C149872217","wikidata":"https://www.wikidata.org/wiki/Q5265701","display_name":"Determining the number of clusters in a data set","level":5,"score":0.5684995651245117},{"id":"https://openalex.org/C105902424","wikidata":"https://www.wikidata.org/wiki/Q1197129","display_name":"Evolutionary computation","level":2,"score":0.5025701522827148},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.4881513714790344},{"id":"https://openalex.org/C176066374","wikidata":"https://www.wikidata.org/wiki/Q629118","display_name":"Fitness function","level":3,"score":0.447751522064209},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4439956843852997},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.4403392970561981},{"id":"https://openalex.org/C164866538","wikidata":"https://www.wikidata.org/wiki/Q367351","display_name":"Cluster (spacecraft)","level":2,"score":0.4339950680732727},{"id":"https://openalex.org/C8880873","wikidata":"https://www.wikidata.org/wiki/Q187787","display_name":"Genetic algorithm","level":2,"score":0.4124661386013031},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.40271803736686707},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3601382374763489},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3354029357433319},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3116919994354248},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.27894914150238037},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2687496840953827},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.20376724004745483},{"id":"https://openalex.org/C104047586","wikidata":"https://www.wikidata.org/wiki/Q5033439","display_name":"Canopy clustering algorithm","level":4,"score":0.17168983817100525},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/cimca.2005.1631337","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cimca.2005.1631337","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06)","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.329.7552","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.329.7552","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.ece.uvic.ca/~wlu/CIMCA05.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W159579334","https://openalex.org/W1493631489","https://openalex.org/W1494306075","https://openalex.org/W1543388142","https://openalex.org/W1554085250","https://openalex.org/W1566480186","https://openalex.org/W1572134371","https://openalex.org/W1824865661","https://openalex.org/W1992419399","https://openalex.org/W2038885294","https://openalex.org/W2051224630","https://openalex.org/W2084812512","https://openalex.org/W2117812871","https://openalex.org/W2145268945","https://openalex.org/W2157665255","https://openalex.org/W2166698530","https://openalex.org/W2333283264","https://openalex.org/W2797746357","https://openalex.org/W6632547301","https://openalex.org/W6683235873"],"related_works":["https://openalex.org/W2893733063","https://openalex.org/W4297582752","https://openalex.org/W4285805405","https://openalex.org/W1634842376","https://openalex.org/W85101999","https://openalex.org/W2550292406","https://openalex.org/W1606502848","https://openalex.org/W2021504082","https://openalex.org/W1583704618","https://openalex.org/W2391924736"],"abstract_inverted_index":{"Estimating":[0],"the":[1,12,23,48,72,81,88],"optimal":[2,89],"number":[3,24,90],"of":[4,11,25,91,96],"clusters":[5,26,92],"for":[6,22,63,93],"a":[7,39,53,94],"dataset":[8,74],"is":[9],"one":[10],"most":[13],"essential":[14],"issues":[15],"in":[16],"cluster":[17],"analysis.":[18],"An":[19],"improper":[20],"pre-selection":[21],"might":[27],"easily":[28],"lead":[29],"to":[30,43],"bad":[31],"clustering":[32],"outcome.":[33],"In":[34],"this":[35,45],"paper,":[36],"we":[37],"propose":[38],"new":[40,54,60],"evolutionary":[41,50,83],"algorithm":[42,51,84],"address":[44],"issue.":[46],"Specifically,":[47],"proposed":[49,82],"defines":[52],"entropy-based":[55],"fitness":[56],"function,":[57],"and":[58,66,75],"three":[59],"genetic":[61],"operators":[62],"splitting,":[64],"merging,":[65],"removing":[67],"clusters.":[68],"Empirical":[69],"evaluations":[70],"using":[71],"synthetic":[73],"an":[76],"existing":[77],"benchmark":[78],"show":[79],"that":[80],"can":[85],"exactly":[86],"estimate":[87],"set":[95],"data":[97]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":1},{"year":2014,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
