{"id":"https://openalex.org/W2810933769","doi":"https://doi.org/10.3233/jifs-169747","title":"Fuzzy clustering algorithm of interactive multi-sensor probabilistic data","display_name":"Fuzzy clustering algorithm of interactive multi-sensor probabilistic data","publication_year":2018,"publication_date":"2018-06-28","ids":{"openalex":"https://openalex.org/W2810933769","doi":"https://doi.org/10.3233/jifs-169747","mag":"2810933769"},"language":"en","primary_location":{"id":"doi:10.3233/jifs-169747","is_oa":false,"landing_page_url":"https://doi.org/10.3233/jifs-169747","pdf_url":null,"source":{"id":"https://openalex.org/S179157397","display_name":"Journal of Intelligent & Fuzzy Systems","issn_l":"1064-1246","issn":["1064-1246","1875-8967"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Intelligent &amp; Fuzzy Systems","raw_type":"journal-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/A5101671531","display_name":"Chengxi Gu","orcid":"https://orcid.org/0000-0002-8881-4388"},"institutions":[{"id":"https://openalex.org/I4210092223","display_name":"Suzhou Vocational University","ror":"https://ror.org/00hn8pj83","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210092223"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chengxi Gu","raw_affiliation_strings":["Department of Computer Engineering, Suzhou Vocational University, Suzhou, China"],"affiliations":[{"raw_affiliation_string":"Department of Computer Engineering, Suzhou Vocational University, Suzhou, China","institution_ids":["https://openalex.org/I4210092223"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101786349","display_name":"Kyuseok Kim","orcid":"https://orcid.org/0009-0004-2369-6335"},"institutions":[{"id":"https://openalex.org/I1333512998","display_name":"United States Census Bureau","ror":"https://ror.org/01qn7cs15","country_code":"US","type":"funder","lineage":["https://openalex.org/I1333512998","https://openalex.org/I1343035065"]},{"id":"https://openalex.org/I4210157790","display_name":"Statistical Research (United States)","ror":"https://ror.org/058eg8775","country_code":"US","type":"company","lineage":["https://openalex.org/I4210157790"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"K.F. Kim","raw_affiliation_strings":["United States Census Bureau, Center for Statistical Research and Methodology (CSRM), Washington, DC, USA"],"affiliations":[{"raw_affiliation_string":"United States Census Bureau, Center for Statistical Research and Methodology (CSRM), Washington, DC, USA","institution_ids":["https://openalex.org/I1333512998","https://openalex.org/I4210157790"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5101671531"],"corresponding_institution_ids":["https://openalex.org/I4210092223"],"apc_list":null,"apc_paid":null,"fwci":0.2667,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.62930095,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":"35","issue":"4","first_page":"4267","last_page":"4275"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13804","display_name":"Physical Activity and Education Research","score":0.9973999857902527,"subfield":{"id":"https://openalex.org/subfields/2312","display_name":"Water Science and Technology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T13804","display_name":"Physical Activity and Education Research","score":0.9973999857902527,"subfield":{"id":"https://openalex.org/subfields/2312","display_name":"Water Science and Technology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13058","display_name":"Soil and Land Suitability Analysis","score":0.9340999722480774,"subfield":{"id":"https://openalex.org/subfields/2308","display_name":"Management, Monitoring, Policy and Law"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7789168357849121},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6126383543014526},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.610378623008728},{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"Fuzzy logic","score":0.548382043838501},{"id":"https://openalex.org/keywords/fuzzy-clustering","display_name":"Fuzzy clustering","score":0.5464273691177368},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5099953413009644},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.46941623091697693},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4237954318523407},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3492307662963867},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33246976137161255}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7789168357849121},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6126383543014526},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.610378623008728},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.548382043838501},{"id":"https://openalex.org/C17212007","wikidata":"https://www.wikidata.org/wiki/Q5511111","display_name":"Fuzzy clustering","level":3,"score":0.5464273691177368},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5099953413009644},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.46941623091697693},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4237954318523407},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3492307662963867},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33246976137161255}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3233/jifs-169747","is_oa":false,"landing_page_url":"https://doi.org/10.3233/jifs-169747","pdf_url":null,"source":{"id":"https://openalex.org/S179157397","display_name":"Journal of Intelligent & Fuzzy Systems","issn_l":"1064-1246","issn":["1064-1246","1875-8967"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Intelligent &amp; Fuzzy Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W618992586","https://openalex.org/W1916286933","https://openalex.org/W1985600833","https://openalex.org/W1989021088","https://openalex.org/W1990532194","https://openalex.org/W2016038648","https://openalex.org/W2020413672","https://openalex.org/W2167872556","https://openalex.org/W2293710996","https://openalex.org/W2336679177","https://openalex.org/W2373238772","https://openalex.org/W2414242250","https://openalex.org/W2507473779","https://openalex.org/W2565531358","https://openalex.org/W2590238838","https://openalex.org/W2607062265","https://openalex.org/W2612993783","https://openalex.org/W2615712379","https://openalex.org/W2624017131","https://openalex.org/W2734508506","https://openalex.org/W2766539765","https://openalex.org/W2769333087","https://openalex.org/W2784923669"],"related_works":["https://openalex.org/W2067418549","https://openalex.org/W2165695836","https://openalex.org/W4220795558","https://openalex.org/W2273660186","https://openalex.org/W4200264217","https://openalex.org/W2072494908","https://openalex.org/W2608738689","https://openalex.org/W2398543122","https://openalex.org/W1987634878","https://openalex.org/W2143402976"],"abstract_inverted_index":{"In":[0],"traditional":[1],"clustering":[2,25,40,133,161,210],"algorithm,":[3],"the":[4,22,29,65,72,75,82,86,98,102,105,109,113,117,122,129,139,155,168,173,182,187,191,196,205],"number":[5,156,169],"of":[6,24,77,81,88,91,97,104,108,116,157,170,190],"classes":[7,158,171],"must":[8],"be":[9],"set":[10],"beforehand":[11],"and":[12,28,74,112,149,172,195,212],"it":[13,150],"is":[14,26,31,47,61,85,101,135,151,163,193,199],"difficult":[15,152],"in":[16,49],"setting":[17],"parameters.":[18],"For":[19],"uncertain":[20],"environment,":[21],"precision":[23],"low":[27],"scalability":[30],"poor.":[32],"To":[33],"address":[34],"these":[35],"problems,":[36],"a":[37],"new":[38],"fuzzy":[39,130],"algorithm":[41,56,134,162,192,207],"for":[42,68],"interactive":[43,93,140],"multi-sensor":[44,141],"probabilistic":[45,89,142],"data":[46,90,115,183],"proposed":[48,136,206],"this":[50,127],"paper.":[51],"The":[52,79,95],"optimal":[53],"hierarchical":[54],"fusion":[55,69,80,87,96,103,106,124],"with":[57],"no":[58],"prior":[59],"knowledge":[60],"used":[62,67,164],"to":[63,71,120,137,153,165],"sort":[64],"sensors":[66],"according":[70],"quality":[73],"importance":[76],"information.":[78],"first":[83,110],"layer":[84,100,111],"two":[92],"sensors.":[94],"second":[99],"results":[107,202],"probability":[114],"other":[118],"sensor":[119,145],"obtain":[121],"final":[123],"results.":[125],"On":[126],"basis,":[128],"C":[131],"mean":[132],"cluster":[138,175],"data.":[143],"Wireless":[144],"networks":[146],"are":[147],"dynamic,":[148],"determine":[154,167],"beforehand.":[159],"Subtraction":[160],"adaptively":[166],"initial":[174],"center":[176],"though":[177],"building":[178],"mountain":[179],"function":[180],"as":[181],"density":[184],"index.":[185],"Thus,":[186],"convergence":[188],"speed":[189],"accelerated":[194],"local":[197],"optimum":[198],"avoided.":[200],"Experimental":[201],"show":[203],"that":[204],"has":[208],"high":[209],"accuracy":[211],"good":[213],"scalability.":[214]},"counts_by_year":[{"year":2020,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
