{"id":"https://openalex.org/W2810703393","doi":"https://doi.org/10.1109/fskd.2017.8392941","title":"On utilization of k-means for determination of q-parameter for tsallis-entropy-maximized-FCM","display_name":"On utilization of k-means for determination of q-parameter for tsallis-entropy-maximized-FCM","publication_year":2017,"publication_date":"2017-07-01","ids":{"openalex":"https://openalex.org/W2810703393","doi":"https://doi.org/10.1109/fskd.2017.8392941","mag":"2810703393"},"language":"en","primary_location":{"id":"doi:10.1109/fskd.2017.8392941","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fskd.2017.8392941","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","raw_type":"proceedings-article"},"type":"article","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/A5101826039","display_name":"Makoto Yasuda","orcid":"https://orcid.org/0000-0001-9619-5073"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Makoto Yasuda","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Gifu College, Gifu, Japan"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Gifu College, Gifu, Japan","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5101826039"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.22272678,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"1","issue":null,"first_page":"1233","last_page":"1240"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12261","display_name":"Statistical Mechanics and Entropy","score":0.9926999807357788,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12261","display_name":"Statistical Mechanics and Entropy","score":0.9926999807357788,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9883000254631042,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12135","display_name":"Fuzzy Systems and Optimization","score":0.95169997215271,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/tsallis-entropy","display_name":"Tsallis entropy","score":0.703191876411438},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6516021490097046},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.591559648513794},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.5026378631591797},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4555465281009674},{"id":"https://openalex.org/keywords/binary-entropy-function","display_name":"Binary entropy function","score":0.44096970558166504},{"id":"https://openalex.org/keywords/simulated-annealing","display_name":"Simulated annealing","score":0.425382137298584},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.41780608892440796},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3991159498691559},{"id":"https://openalex.org/keywords/statistical-physics","display_name":"Statistical physics","score":0.36577367782592773},{"id":"https://openalex.org/keywords/discrete-mathematics","display_name":"Discrete mathematics","score":0.35455334186553955},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.34802666306495667},{"id":"https://openalex.org/keywords/principle-of-maximum-entropy","display_name":"Principle of maximum entropy","score":0.33471664786338806},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.32900506258010864},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.22028261423110962},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.20882609486579895},{"id":"https://openalex.org/keywords/tsallis-statistics","display_name":"Tsallis statistics","score":0.1968672275543213},{"id":"https://openalex.org/keywords/thermodynamics","display_name":"Thermodynamics","score":0.1357625424861908},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.13368293642997742}],"concepts":[{"id":"https://openalex.org/C117521176","wikidata":"https://www.wikidata.org/wiki/Q7849341","display_name":"Tsallis entropy","level":3,"score":0.703191876411438},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6516021490097046},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.591559648513794},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.5026378631591797},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4555465281009674},{"id":"https://openalex.org/C44415725","wikidata":"https://www.wikidata.org/wiki/Q4913893","display_name":"Binary entropy function","level":3,"score":0.44096970558166504},{"id":"https://openalex.org/C126980161","wikidata":"https://www.wikidata.org/wiki/Q863783","display_name":"Simulated annealing","level":2,"score":0.425382137298584},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.41780608892440796},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3991159498691559},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.36577367782592773},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.35455334186553955},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.34802666306495667},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.33471664786338806},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.32900506258010864},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.22028261423110962},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.20882609486579895},{"id":"https://openalex.org/C2780056601","wikidata":"https://www.wikidata.org/wiki/Q7849339","display_name":"Tsallis statistics","level":2,"score":0.1968672275543213},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.1357625424861908},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.13368293642997742}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/fskd.2017.8392941","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fskd.2017.8392941","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W135805903","https://openalex.org/W1763348573","https://openalex.org/W1983874169","https://openalex.org/W2024060531","https://openalex.org/W2036216970","https://openalex.org/W2040951443","https://openalex.org/W2077096052","https://openalex.org/W2113076747","https://openalex.org/W2127218421","https://openalex.org/W2161877964","https://openalex.org/W2196992208","https://openalex.org/W2396266686","https://openalex.org/W2563473706","https://openalex.org/W6637688416","https://openalex.org/W6678914141","https://openalex.org/W6687523247"],"related_works":["https://openalex.org/W2113282606","https://openalex.org/W2047083630","https://openalex.org/W4247532445","https://openalex.org/W1986828803","https://openalex.org/W2945554820","https://openalex.org/W2575378359","https://openalex.org/W2963163002","https://openalex.org/W4297751392","https://openalex.org/W2005137334","https://openalex.org/W1672097335"],"abstract_inverted_index":{"In":[0,114],"this":[1,64,97],"article,":[2,98],"we":[3,99],"consider":[4],"a":[5,29,91,101,125,138,143,174],"combinatorial":[6],"algorithm":[7,134],"of":[8,32,55,58,145],"fuzzy":[9],"c-means":[10],"(FCM)":[11],"clustering":[12,133],"maximized":[13],"with":[14],"the":[15,19,33,49,53,59,79,117,131,153],"Tsallis":[16,24,50],"entropy":[17,25,51],"and":[18,78,130,158,161,171],"deterministic":[20],"annealing":[21,83],"method.":[22],"The":[23,148],"is":[26,68,120,135,156],"determined":[27,168],"as":[28,137],"q-parameter":[30],"extension":[31],"Shannon":[34],"entropy.":[35],"Membership":[36],"functions":[37,43],"similar":[38],"to":[39,72,141],"statistical":[40],"mechanical":[41],"distribution":[42],"have":[44],"been":[45],"derived":[46],"by":[47,122,124],"maximizing":[48],"within":[52],"framework":[54],"FCM.":[56],"One":[57],"major":[60],"issues":[61],"when":[62],"using":[63],"method":[65,103,129,155],"is,":[66],"it":[67],"still":[69],"unknown":[70],"how":[71],"determine":[73],"an":[74],"appropriate":[75],"q":[76,160],"value":[77],"initial":[80],"or":[81],"highest":[82],"temperature":[84],"(T":[85],"<sub":[86,163],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[87,164],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">high</sub>":[88,165],")":[89],"for":[90,173],"given":[92,175],"data":[93,176],"set.":[94,177],"Accordingly,":[95],"in":[96],"present":[100],"new":[102],"that":[104,152],"determines":[105],"these":[106],"values":[107],"simultaneously":[108],"without":[109],"introducing":[110],"any":[111],"additional":[112],"parameters.":[113],"our":[115],"method,":[116],"membership":[118],"function":[119],"simplified":[121],"approximating":[123],"high-temperature":[126],"series":[127],"expansion":[128],"k-means":[132],"utilized":[136],"preprocessing":[139],"step":[140],"estimate":[142],"radius":[144],"each":[146],"cluster.":[147],"experimental":[149],"results":[150],"indicate":[151],"proposed":[154],"effective,":[157],"both":[159],"T":[162],"can":[166],"be":[167],"automatically,":[169],"algebraically":[170],"properly":[172]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
