{"id":"https://openalex.org/W4414123512","doi":"https://doi.org/10.1109/fuzz62266.2025.11152086","title":"Proposed Bounded Variation Based Probabilistic Intuitionistic Fuzzy C-Means Clustering Algorithm","display_name":"Proposed Bounded Variation Based Probabilistic Intuitionistic Fuzzy C-Means Clustering Algorithm","publication_year":2025,"publication_date":"2025-07-06","ids":{"openalex":"https://openalex.org/W4414123512","doi":"https://doi.org/10.1109/fuzz62266.2025.11152086"},"language":"en","primary_location":{"id":"doi:10.1109/fuzz62266.2025.11152086","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fuzz62266.2025.11152086","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Fuzzy Systems (FUZZ)","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/A5064039099","display_name":"Niher Ranjan Das","orcid":null},"institutions":[{"id":"https://openalex.org/I90425906","display_name":"South Asian University","ror":"https://ror.org/02kjyst95","country_code":"IN","type":"education","lineage":["https://openalex.org/I90425906"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Niher Ranjan Das","raw_affiliation_strings":["South Asian University,Department of Mathematics,New Delhi,India"],"affiliations":[{"raw_affiliation_string":"South Asian University,Department of Mathematics,New Delhi,India","institution_ids":["https://openalex.org/I90425906"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072985982","display_name":"Preeti Singh","orcid":"https://orcid.org/0000-0003-4680-6299"},"institutions":[{"id":"https://openalex.org/I90425906","display_name":"South Asian University","ror":"https://ror.org/02kjyst95","country_code":"IN","type":"education","lineage":["https://openalex.org/I90425906"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Prathvi Raj Singh","raw_affiliation_strings":["South Asian University,Department of Mathematics,New Delhi,India"],"affiliations":[{"raw_affiliation_string":"South Asian University,Department of Mathematics,New Delhi,India","institution_ids":["https://openalex.org/I90425906"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044865157","display_name":"Q. M. Danish Lohani","orcid":"https://orcid.org/0000-0002-2006-3709"},"institutions":[{"id":"https://openalex.org/I90425906","display_name":"South Asian University","ror":"https://ror.org/02kjyst95","country_code":"IN","type":"education","lineage":["https://openalex.org/I90425906"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Q. M. Danish Lohani","raw_affiliation_strings":["South Asian University,Department of Mathematics,New Delhi,India"],"affiliations":[{"raw_affiliation_string":"South Asian University,Department of Mathematics,New Delhi,India","institution_ids":["https://openalex.org/I90425906"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5064039099"],"corresponding_institution_ids":["https://openalex.org/I90425906"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.29898121,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10050","display_name":"Multi-Criteria Decision Making","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10050","display_name":"Multi-Criteria Decision Making","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10820","display_name":"Fuzzy Logic and Control Systems","score":0.9950000047683716,"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/T11063","display_name":"Rough Sets and Fuzzy Logic","score":0.9901000261306763,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/probabilistic-logic","display_name":"Probabilistic logic","score":0.661300003528595},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.65420001745224},{"id":"https://openalex.org/keywords/fuzzy-clustering","display_name":"Fuzzy clustering","score":0.52920001745224},{"id":"https://openalex.org/keywords/bounded-function","display_name":"Bounded function","score":0.4943000078201294},{"id":"https://openalex.org/keywords/partition","display_name":"Partition (number theory)","score":0.42750000953674316},{"id":"https://openalex.org/keywords/fuzzy-set","display_name":"Fuzzy set","score":0.41040000319480896},{"id":"https://openalex.org/keywords/constrained-clustering","display_name":"Constrained clustering","score":0.4043999910354614},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39329999685287476},{"id":"https://openalex.org/keywords/distance-measures","display_name":"Distance measures","score":0.39309999346733093}],"concepts":[{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.661300003528595},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.65420001745224},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5663999915122986},{"id":"https://openalex.org/C17212007","wikidata":"https://www.wikidata.org/wiki/Q5511111","display_name":"Fuzzy clustering","level":3,"score":0.52920001745224},{"id":"https://openalex.org/C34388435","wikidata":"https://www.wikidata.org/wiki/Q2267362","display_name":"Bounded function","level":2,"score":0.4943000078201294},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4399000108242035},{"id":"https://openalex.org/C42812","wikidata":"https://www.wikidata.org/wiki/Q1082910","display_name":"Partition (number theory)","level":2,"score":0.42750000953674316},{"id":"https://openalex.org/C42011625","wikidata":"https://www.wikidata.org/wiki/Q1055058","display_name":"Fuzzy set","level":3,"score":0.41040000319480896},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4058000147342682},{"id":"https://openalex.org/C27964816","wikidata":"https://www.wikidata.org/wiki/Q5164359","display_name":"Constrained clustering","level":5,"score":0.4043999910354614},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39329999685287476},{"id":"https://openalex.org/C2639959","wikidata":"https://www.wikidata.org/wiki/Q1344778","display_name":"Distance measures","level":2,"score":0.39309999346733093},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.3928999900817871},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.3788999915122986},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3695000112056732},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.3671000003814697},{"id":"https://openalex.org/C120174047","wikidata":"https://www.wikidata.org/wiki/Q847073","display_name":"Euclidean distance","level":2,"score":0.36660000681877136},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3573000133037567},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.35249999165534973},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.3280999958515167},{"id":"https://openalex.org/C104047586","wikidata":"https://www.wikidata.org/wiki/Q5033439","display_name":"Canopy clustering algorithm","level":4,"score":0.32269999384880066},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.32190001010894775},{"id":"https://openalex.org/C186633575","wikidata":"https://www.wikidata.org/wiki/Q845060","display_name":"Maxima and minima","level":2,"score":0.3165000081062317},{"id":"https://openalex.org/C22648726","wikidata":"https://www.wikidata.org/wiki/Q7523744","display_name":"Single-linkage clustering","level":5,"score":0.31540000438690186},{"id":"https://openalex.org/C115328559","wikidata":"https://www.wikidata.org/wiki/Q4041956","display_name":"k-medians clustering","level":5,"score":0.3059999942779541},{"id":"https://openalex.org/C33704608","wikidata":"https://www.wikidata.org/wiki/Q5014717","display_name":"CURE data clustering algorithm","level":4,"score":0.30149999260902405},{"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":0.2935999929904938},{"id":"https://openalex.org/C44859942","wikidata":"https://www.wikidata.org/wiki/Q5426511","display_name":"FLAME clustering","level":5,"score":0.2883000075817108},{"id":"https://openalex.org/C2778334786","wikidata":"https://www.wikidata.org/wiki/Q1586270","display_name":"Variation (astronomy)","level":2,"score":0.26409998536109924},{"id":"https://openalex.org/C164866538","wikidata":"https://www.wikidata.org/wiki/Q367351","display_name":"Cluster (spacecraft)","level":2,"score":0.2635999917984009},{"id":"https://openalex.org/C2778865114","wikidata":"https://www.wikidata.org/wiki/Q7882489","display_name":"Uncertain data","level":2,"score":0.25119999051094055}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/fuzz62266.2025.11152086","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fuzz62266.2025.11152086","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Fuzzy Systems (FUZZ)","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":21,"referenced_works":["https://openalex.org/W343066586","https://openalex.org/W1578645468","https://openalex.org/W1964473818","https://openalex.org/W1966808269","https://openalex.org/W1971875039","https://openalex.org/W1994940876","https://openalex.org/W1998274657","https://openalex.org/W2001094877","https://openalex.org/W2005899786","https://openalex.org/W2036809061","https://openalex.org/W2048819046","https://openalex.org/W2053677366","https://openalex.org/W2170853747","https://openalex.org/W2809682588","https://openalex.org/W2949693766","https://openalex.org/W3082120102","https://openalex.org/W3104232778","https://openalex.org/W4211007335","https://openalex.org/W4292295629","https://openalex.org/W4295768206","https://openalex.org/W4391544001"],"related_works":["https://openalex.org/W2945382830","https://openalex.org/W4224807364","https://openalex.org/W2596632494","https://openalex.org/W2535986621","https://openalex.org/W1980197432","https://openalex.org/W2382432689","https://openalex.org/W2000612978","https://openalex.org/W4388110928","https://openalex.org/W1483228865","https://openalex.org/W4292434959"],"abstract_inverted_index":{"Euclidean":[0],"distance":[1,63,77,113],"measures":[2,64,114],"are":[3,54,58,106,166],"mostly":[4],"utilized":[5],"in":[6,50,102,133],"proposing":[7],"probabilistic":[8,31,84,91],"variants":[9],"of":[10,62],"local":[11,45],"optima-based":[12],"intuitionistic":[13,32,92],"fuzzy":[14,33,93],"c-mean":[15,34,68,94],"clustering":[16,20,40],"algorithms.":[17,160],"The":[18,30],"partitional":[19],"algorithm":[21,35,41,95,155],"involving":[22,79],"global":[23],"optima":[24],"is":[25,37,99],"missing":[26],"from":[27],"the":[28,51,67,88,117,142,162],"literature.":[29],"(PIFCM)":[36],"a":[38,44,75],"well-known":[39],"converging":[42],"to":[43,65,86],"minima":[46],"and":[47,83,126,146,158,170],"hence":[48,127],"variations":[49],"optimal":[52],"outcomes":[53],"observed.":[55],"So,":[56],"researchers":[57],"using":[59],"several":[60],"types":[61],"improvise":[66],"algorithm.":[69],"In":[70],"this":[71],"paper,":[72],"we":[73],"exploit":[74],"step-based":[76],"measure":[78],"bounded":[80,89],"variation":[81],"(BV)":[82],"weights":[85],"propose":[87],"variation-based":[90],"(BV-PIFCM).":[96],"This":[97],"approach":[98],"particularly":[100],"effective":[101],"datasets":[103,150],"where":[104],"clusters":[105],"non-globular":[107],"or":[108,136],"have":[109],"irregular":[110,124],"shapes.":[111],"Step-Based":[112],"nearly":[115],"capture":[116],"realistic":[118],"scenarios":[119],"as":[120,173],"they":[121],"move":[122],"along":[123],"paths,":[125],"cluster":[128,171],"detection":[129],"becomes":[130],"easy":[131],"even":[132],"varying":[134],"densities":[135],"sparse":[137],"regions.":[138],"Experimental":[139],"results":[140],"on":[141],"car":[143],"data":[144],"set":[145],"multiple":[147],"benchmark":[148,163],"UCI":[149],"demonstrate":[151],"that":[152],"our":[153],"proposed":[154],"outperforms":[156],"PIFCM":[157],"IFCM":[159],"Here,":[161],"indexes":[164],"used":[165],"accuracy,":[167],"partition":[168],"coefficients,":[169],"entropy":[172],"performance":[174],"metrics.":[175]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-10T00:00:00"}