{"id":"https://openalex.org/W7130308952","doi":"https://doi.org/10.1109/tencon66050.2025.11375049","title":"Preserving Cluster Identity Across Time: an Incremental Cosine Similarity Approach","display_name":"Preserving Cluster Identity Across Time: an Incremental Cosine Similarity Approach","publication_year":2025,"publication_date":"2025-10-27","ids":{"openalex":"https://openalex.org/W7130308952","doi":"https://doi.org/10.1109/tencon66050.2025.11375049"},"language":null,"primary_location":{"id":"doi:10.1109/tencon66050.2025.11375049","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tencon66050.2025.11375049","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"TENCON 2025 - 2025 IEEE Region 10 Conference (TENCON)","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/A5126310791","display_name":"Adarsh Nair","orcid":null},"institutions":[{"id":"https://openalex.org/I4210139993","display_name":"Global Services (Slovakia)","ror":"https://ror.org/047kbmp67","country_code":"SK","type":"company","lineage":["https://openalex.org/I4210139993"]}],"countries":["SK"],"is_corresponding":false,"raw_author_name":"Adarsh Nair","raw_affiliation_strings":["Data Science Yum India Global Services Private Limited,Gurgaon,India"],"raw_orcid":"https://orcid.org/0000-0002-6092-2352","affiliations":[{"raw_affiliation_string":"Data Science Yum India Global Services Private Limited,Gurgaon,India","institution_ids":["https://openalex.org/I4210139993"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5126306796","display_name":"K.K. Aishwariya","orcid":null},"institutions":[{"id":"https://openalex.org/I4210129961","display_name":"IBM (India)","ror":"https://ror.org/034ahpr11","country_code":"IN","type":"company","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210129961"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"K.K. Aishwariya","raw_affiliation_strings":["IBM India Pvt. Ltd,Bengaluru,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM India Pvt. Ltd,Bengaluru,India","institution_ids":["https://openalex.org/I4210129961"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.73290155,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1053","last_page":"1057"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":0.1703999936580658,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":0.1703999936580658,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.11509999632835388,"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":0.09650000184774399,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/cluster","display_name":"Cluster (spacecraft)","score":0.7652999758720398},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7355999946594238},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6323000192642212},{"id":"https://openalex.org/keywords/cosine-similarity","display_name":"Cosine similarity","score":0.5321999788284302},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5045999884605408},{"id":"https://openalex.org/keywords/complete-linkage-clustering","display_name":"Complete-linkage clustering","score":0.45210000872612},{"id":"https://openalex.org/keywords/trigonometric-functions","display_name":"Trigonometric functions","score":0.38679999113082886}],"concepts":[{"id":"https://openalex.org/C164866538","wikidata":"https://www.wikidata.org/wiki/Q367351","display_name":"Cluster (spacecraft)","level":2,"score":0.7652999758720398},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7355999946594238},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6323000192642212},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6043999791145325},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5539000034332275},{"id":"https://openalex.org/C2780762811","wikidata":"https://www.wikidata.org/wiki/Q1784941","display_name":"Cosine similarity","level":3,"score":0.5321999788284302},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5045999884605408},{"id":"https://openalex.org/C23822008","wikidata":"https://www.wikidata.org/wiki/Q5156437","display_name":"Complete-linkage clustering","level":5,"score":0.45210000872612},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4287000000476837},{"id":"https://openalex.org/C178009071","wikidata":"https://www.wikidata.org/wiki/Q93344","display_name":"Trigonometric functions","level":2,"score":0.38679999113082886},{"id":"https://openalex.org/C2778355321","wikidata":"https://www.wikidata.org/wiki/Q17079427","display_name":"Identity (music)","level":2,"score":0.37369999289512634},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33719998598098755},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.3314000070095062},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3059000074863434},{"id":"https://openalex.org/C22648726","wikidata":"https://www.wikidata.org/wiki/Q7523744","display_name":"Single-linkage clustering","level":5,"score":0.29429998993873596},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2890999913215637},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.26840001344680786},{"id":"https://openalex.org/C111442797","wikidata":"https://www.wikidata.org/wiki/Q7291446","display_name":"Rand index","level":3,"score":0.2621000111103058},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.2597000002861023},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2574000060558319}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tencon66050.2025.11375049","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tencon66050.2025.11375049","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"TENCON 2025 - 2025 IEEE Region 10 Conference (TENCON)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W1998871699","https://openalex.org/W2040466507","https://openalex.org/W2095897464","https://openalex.org/W2170936641","https://openalex.org/W4407735769"],"related_works":[],"abstract_inverted_index":{"In":[0,40],"many":[1],"real":[2,122],"world":[3,123],"applications,":[4],"such":[5],"as":[6,37],"customer":[7],"behavior":[8],"analysis,":[9],"data":[10,62],"distributions":[11],"evolve":[12],"gradually":[13],"over":[14,106],"time.":[15],"Traditional":[16],"clustering":[17,47],"methods,":[18],"which":[19],"retrain":[20],"models":[21],"from":[22],"scratch":[23],"at":[24],"regular":[25],"intervals,":[26],"often":[27],"fail":[28],"to":[29,71],"preserve":[30],"the":[31,67,88,126],"fine":[32],"grained":[33],"dynamics":[34],"within":[35],"clusters":[36,59,69,73],"they":[38],"evolve.":[39],"this":[41],"paper,":[42],"we":[43],"propose":[44],"an":[45],"incremental":[46],"framework":[48,140],"that":[49],"maintains":[50],"cluster":[51,79,89,95,104,112,133,154],"continuity":[52],"across":[53],"sequential":[54],"time":[55,148],"periods.":[56],"Our":[57,139],"approach":[58,130],"new":[60,94],"incoming":[61],"independently":[63],"and":[64,121,135],"then":[65],"maps":[66],"resulting":[68],"back":[70],"existing":[72],"by":[74],"computing":[75],"cosine":[76],"similarity":[77],"between":[78],"centroids.":[80],"If":[81],"a":[82,93,142],"sufficiently":[83],"similar":[84],"match":[85],"is":[86,90,96,156],"found,":[87],"continued;":[91],"otherwise,":[92],"initialized.":[97],"This":[98],"method":[99],"enables":[100],"accurate":[101],"tracking":[102],"of":[103,128],"evolution":[105],"time,":[107],"capturing":[108],"subtle":[109],"shifts":[110],"in":[111,131],"characteristics":[113],"without":[114],"abrupt":[115],"reassignments.":[116],"Experimental":[117],"results":[118],"on":[119],"synthetic":[120],"datasets":[124],"demonstrate":[125],"effectiveness":[127],"our":[129],"preserving":[132],"lineage":[134],"detecting":[136],"emerging":[137],"behaviors.":[138],"offers":[141],"simple":[143],"yet":[144],"powerful":[145],"solution":[146],"for":[147],"sensitive":[149],"applications":[150],"where":[151],"maintaining":[152],"historical":[153],"context":[155],"critical.":[157]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-02-19T00:00:00"}