{"id":"https://openalex.org/W2529414467","doi":"https://doi.org/10.1109/dsmp.2016.7583499","title":"Inductive model of data clustering based on the agglomerative hierarchical algorithm","display_name":"Inductive model of data clustering based on the agglomerative hierarchical algorithm","publication_year":2016,"publication_date":"2016-08-01","ids":{"openalex":"https://openalex.org/W2529414467","doi":"https://doi.org/10.1109/dsmp.2016.7583499","mag":"2529414467"},"language":"en","primary_location":{"id":"doi:10.1109/dsmp.2016.7583499","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dsmp.2016.7583499","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE First International Conference on Data Stream Mining &amp; Processing (DSMP)","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/A5076340310","display_name":"Sergii Babichev","orcid":"https://orcid.org/0000-0001-6797-1467"},"institutions":[{"id":"https://openalex.org/I3122934890","display_name":"Jan Evangelista Purkyn\u011b University in \u00dast\u00ed nad Labem","ror":"https://ror.org/04vjwcp92","country_code":"CZ","type":"education","lineage":["https://openalex.org/I3122934890"]}],"countries":["CZ"],"is_corresponding":true,"raw_author_name":"Sergii Babichev","raw_affiliation_strings":["Jan Evangelista Purkyne University in Usti nad Labem, Usti nad Labem, Czech Republic"],"affiliations":[{"raw_affiliation_string":"Jan Evangelista Purkyne University in Usti nad Labem, Usti nad Labem, Czech Republic","institution_ids":["https://openalex.org/I3122934890"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085563160","display_name":"Mohamed Ali Taif","orcid":"https://orcid.org/0000-0002-3449-6791"},"institutions":[{"id":"https://openalex.org/I118493624","display_name":"Kherson National Technical University","ror":"https://ror.org/04trbj059","country_code":"UA","type":"education","lineage":["https://openalex.org/I118493624"]}],"countries":["UA"],"is_corresponding":false,"raw_author_name":"Mohamed Ali Taif","raw_affiliation_strings":["Kherson National Technical University, Kherson, Ukraine"],"affiliations":[{"raw_affiliation_string":"Kherson National Technical University, Kherson, Ukraine","institution_ids":["https://openalex.org/I118493624"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5033022212","display_name":"Volodymyr Lytvynenko","orcid":"https://orcid.org/0000-0002-1536-5542"},"institutions":[{"id":"https://openalex.org/I118493624","display_name":"Kherson National Technical University","ror":"https://ror.org/04trbj059","country_code":"UA","type":"education","lineage":["https://openalex.org/I118493624"]}],"countries":["UA"],"is_corresponding":false,"raw_author_name":"Volodymyr Lytvynenko","raw_affiliation_strings":["Kherson National Technical University, Kherson, Ukraine"],"affiliations":[{"raw_affiliation_string":"Kherson National Technical University, Kherson, Ukraine","institution_ids":["https://openalex.org/I118493624"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5076340310"],"corresponding_institution_ids":["https://openalex.org/I3122934890"],"apc_list":null,"apc_paid":null,"fwci":5.9987,"has_fulltext":false,"cited_by_count":25,"citation_normalized_percentile":{"value":0.9644841,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"19","last_page":"22"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14351","display_name":"Statistical and Computational Modeling","score":0.9973999857902527,"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/T14351","display_name":"Statistical and Computational Modeling","score":0.9973999857902527,"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/T14438","display_name":"Advanced Scientific Research Methods","score":0.9854999780654907,"subfield":{"id":"https://openalex.org/subfields/1106","display_name":"Food Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T14069","display_name":"Scientific Research Methodologies and Applications","score":0.9732999801635742,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.832439661026001},{"id":"https://openalex.org/keywords/hierarchical-clustering","display_name":"Hierarchical clustering","score":0.7603256702423096},{"id":"https://openalex.org/keywords/single-linkage-clustering","display_name":"Single-linkage clustering","score":0.6663805842399597},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6112902164459229},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5693565011024475},{"id":"https://openalex.org/keywords/hierarchical-clustering-of-networks","display_name":"Hierarchical clustering of networks","score":0.5376665592193604},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.5278912782669067},{"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":0.5194308757781982},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.48100194334983826},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.44880372285842896},{"id":"https://openalex.org/keywords/cure-data-clustering-algorithm","display_name":"CURE data clustering algorithm","score":0.44450661540031433},{"id":"https://openalex.org/keywords/canopy-clustering-algorithm","display_name":"Canopy clustering algorithm","score":0.4340459108352661},{"id":"https://openalex.org/keywords/hierarchical-database-model","display_name":"Hierarchical database model","score":0.43129411339759827},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.4278582036495209},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.367044061422348},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3509727716445923}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.832439661026001},{"id":"https://openalex.org/C92835128","wikidata":"https://www.wikidata.org/wiki/Q1277447","display_name":"Hierarchical clustering","level":3,"score":0.7603256702423096},{"id":"https://openalex.org/C22648726","wikidata":"https://www.wikidata.org/wiki/Q7523744","display_name":"Single-linkage clustering","level":5,"score":0.6663805842399597},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6112902164459229},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5693565011024475},{"id":"https://openalex.org/C82261393","wikidata":"https://www.wikidata.org/wiki/Q17038699","display_name":"Hierarchical clustering of networks","level":5,"score":0.5376665592193604},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.5278912782669067},{"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":0.5194308757781982},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.48100194334983826},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.44880372285842896},{"id":"https://openalex.org/C33704608","wikidata":"https://www.wikidata.org/wiki/Q5014717","display_name":"CURE data clustering algorithm","level":4,"score":0.44450661540031433},{"id":"https://openalex.org/C104047586","wikidata":"https://www.wikidata.org/wiki/Q5033439","display_name":"Canopy clustering algorithm","level":4,"score":0.4340459108352661},{"id":"https://openalex.org/C144986985","wikidata":"https://www.wikidata.org/wiki/Q871236","display_name":"Hierarchical database model","level":2,"score":0.43129411339759827},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4278582036495209},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.367044061422348},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3509727716445923},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/dsmp.2016.7583499","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dsmp.2016.7583499","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE First International Conference on Data Stream Mining &amp; Processing (DSMP)","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":3,"referenced_works":["https://openalex.org/W1553939018","https://openalex.org/W1972969203","https://openalex.org/W2295256067"],"related_works":["https://openalex.org/W2384052049","https://openalex.org/W2385630304","https://openalex.org/W2342518500","https://openalex.org/W2103650652","https://openalex.org/W2380223361","https://openalex.org/W2559422900","https://openalex.org/W4220814143","https://openalex.org/W4241767317","https://openalex.org/W2365091638","https://openalex.org/W2375682798"],"abstract_inverted_index":{"Model":[0],"of":[1,10,18,28,40,54,58,63,77,88,92,112],"data":[2,84,114],"clustering":[3,29,101,111],"system":[4],"based":[5],"on":[6],"the":[7,24,38,49,51,55,66,71,75,78,93,109],"complex":[8,19,41,97],"use":[9,39],"agglomerative":[11],"hierarchical":[12],"algorithm":[13],"and":[14,61,95],"inductive":[15],"modeling":[16],"methods":[17],"systems":[20],"is":[21,30],"presented":[22],"in":[23,65],"paper.":[25],"The":[26],"quality":[27],"evaluated":[31],"by":[32],"two":[33],"equal":[34],"power":[35],"subsets":[36,60],"with":[37],"balance":[42],"criterion,":[43],"which":[44,105],"takes":[45],"into":[46],"account":[47],"both":[48],"displacement":[50],"mass":[52,72],"centers":[53],"appropriate":[56,67],"clusters":[57,68,87],"different":[59,89],"distribution":[62],"objects":[64],"relative":[69],"to":[70,107],"center.":[73],"Evaluating":[74],"effectiveness":[76],"proposed":[79],"model":[80],"was":[81],"performed":[82],"using":[83],"sets":[85],"containing":[86],"shapes.":[90],"Charts":[91],"external":[94],"internal":[96],"criterion":[98],"values":[99],"against":[100],"level":[102],"were":[103],"created,":[104],"allows":[106],"determine":[108],"optimal":[110],"a":[113],"set.":[115]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":6}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
