{"id":"https://openalex.org/W4402351410","doi":"https://doi.org/10.1109/ijcnn60899.2024.10650134","title":"Clustering by Learning the Ordinal Relationships of Qualitative Attribute Values","display_name":"Clustering by Learning the Ordinal Relationships of Qualitative Attribute Values","publication_year":2024,"publication_date":"2024-06-30","ids":{"openalex":"https://openalex.org/W4402351410","doi":"https://doi.org/10.1109/ijcnn60899.2024.10650134"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn60899.2024.10650134","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn60899.2024.10650134","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","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/A5024777212","display_name":"Pengkai Wang","orcid":"https://orcid.org/0009-0004-0946-0000"},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Pengkai Wang","raw_affiliation_strings":["Guangdong University of Technology,China"],"affiliations":[{"raw_affiliation_string":"Guangdong University of Technology,China","institution_ids":["https://openalex.org/I139024713"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5119962963","display_name":"Yunfan Zhang","orcid":"https://orcid.org/0009-0004-4101-2507"},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunfan Zhang","raw_affiliation_strings":["Guangdong University of Technology,China"],"affiliations":[{"raw_affiliation_string":"Guangdong University of Technology,China","institution_ids":["https://openalex.org/I139024713"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100329232","display_name":"Yiqun Zhang","orcid":"https://orcid.org/0000-0002-0328-987X"},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yiqun Zhang","raw_affiliation_strings":["Guangdong University of Technology,China"],"affiliations":[{"raw_affiliation_string":"Guangdong University of Technology,China","institution_ids":["https://openalex.org/I139024713"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015745132","display_name":"Yang Lu","orcid":"https://orcid.org/0000-0002-3497-9611"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Lu","raw_affiliation_strings":["Xiamen University,China"],"affiliations":[{"raw_affiliation_string":"Xiamen University,China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100730941","display_name":"Mengke Li","orcid":"https://orcid.org/0000-0002-9433-9683"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mengke Li","raw_affiliation_strings":["Shenzhen University,Guangming Lab,China"],"affiliations":[{"raw_affiliation_string":"Shenzhen University,Guangming Lab,China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038516431","display_name":"Yiu\u2010ming Cheung","orcid":"https://orcid.org/0000-0001-7629-4648"},"institutions":[{"id":"https://openalex.org/I141568987","display_name":"Hong Kong Baptist University","ror":"https://ror.org/0145fw131","country_code":"HK","type":"education","lineage":["https://openalex.org/I141568987"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Yiu-ming Cheung","raw_affiliation_strings":["Hong Kong Baptist University,Hong Kong"],"affiliations":[{"raw_affiliation_string":"Hong Kong Baptist University,Hong Kong","institution_ids":["https://openalex.org/I141568987"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5024777212"],"corresponding_institution_ids":["https://openalex.org/I139024713"],"apc_list":null,"apc_paid":null,"fwci":1.9896,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.88436091,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9932000041007996,"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9932000041007996,"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/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9876999855041504,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9825999736785889,"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-analysis","display_name":"Cluster analysis","score":0.808513343334198},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6223905086517334},{"id":"https://openalex.org/keywords/ordinal-data","display_name":"Ordinal data","score":0.4372140169143677},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.42083248496055603},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3789220154285431},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2908223271369934}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.808513343334198},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6223905086517334},{"id":"https://openalex.org/C85461838","wikidata":"https://www.wikidata.org/wiki/Q7100785","display_name":"Ordinal data","level":2,"score":0.4372140169143677},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42083248496055603},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3789220154285431},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2908223271369934}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn60899.2024.10650134","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn60899.2024.10650134","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.4000000059604645}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321921","display_name":"Natural Science Foundation of Guangdong Province","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W1879447438","https://openalex.org/W2020344074","https://openalex.org/W2031569733","https://openalex.org/W2040138869","https://openalex.org/W2047878524","https://openalex.org/W2072017174","https://openalex.org/W2107303832","https://openalex.org/W2120887445","https://openalex.org/W2122943553","https://openalex.org/W2132149726","https://openalex.org/W2143687373","https://openalex.org/W2148425841","https://openalex.org/W2338257905","https://openalex.org/W2635535303","https://openalex.org/W2743926534","https://openalex.org/W2913668833","https://openalex.org/W2925162041","https://openalex.org/W2997152122","https://openalex.org/W2997546679","https://openalex.org/W3017786722","https://openalex.org/W3043922872","https://openalex.org/W3128396846","https://openalex.org/W3199244260","https://openalex.org/W4285601133","https://openalex.org/W4295308409","https://openalex.org/W4312457863","https://openalex.org/W4319586703","https://openalex.org/W4390143667","https://openalex.org/W4390971060","https://openalex.org/W4399686532","https://openalex.org/W4401752853","https://openalex.org/W6636975626"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W2382290278","https://openalex.org/W4395014643"],"abstract_inverted_index":{"In":[0],"many":[1],"real-world":[2],"clustering":[3,48,79,85,128,162],"tasks,":[4],"data":[5],"objects":[6],"are":[7,21,25],"described":[8],"by":[9],"both":[10],"quantitative":[11],"and":[12,24,81,111,127],"qualitative":[13,19,146,165],"attributes.":[14],"Attributes":[15],"with":[16,45,64,117,151],"semantically":[17],"ordered":[18],"values":[20],"very":[22],"common":[23],"usually":[26],"coded":[27],"according":[28],"to":[29,78,99,141],"their":[30],"order":[31,39,73,126],"(i.e.,":[32],"consecutive":[33],"integers)":[34],"for":[35],"clustering.":[36],"However,":[37],"semantic":[38],"is":[40,53,59,137],"not":[41,60],"always":[42,61],"globally":[43],"interdependent":[44],"a":[46,76,95],"certain":[47],"task.":[49,129],"An":[50],"intuitive":[51],"case":[52],"that":[54,155],"level":[55,66],"of":[56,67,89,133],"income":[57],"(attribute)":[58],"positively":[62],"correlated":[63],"the":[65,83,101,108,122,131,138,159],"mental":[68],"health":[69],"(label).":[70],"Using":[71],"mismatched":[72],"surely":[74],"forms":[75],"bottleneck":[77],"performance,":[80],"conversely,":[82],"unsupervised":[84],"process":[86,114],"prevents":[87],"understanding":[88],"\"true\"":[90],"order.":[91,103],"Therefore,":[92],"we":[93,106],"proposed":[94],"novel":[96],"learning":[97],"paradigm":[98],"tune":[100],"value":[102,125],"More":[104],"specifically,":[105],"adjust":[107],"intra-attribute":[109],"orders,":[110],"let":[112],"this":[113,136],"learn":[115,142],"mutually":[116],"object":[118],"clustering,":[119],"thus":[120],"bridging":[121],"gap":[123],"between":[124],"To":[130],"best":[132],"our":[134,156],"knowledge,":[135],"first":[139],"attempt":[140],"ordinal":[143],"relationships":[144],"among":[145],"attribute":[147,166],"values.":[148],"Extensive":[149],"experiments":[150],"significance":[152],"tests":[153],"show":[154],"method":[157],"outperforms":[158],"existing":[160],"relevant":[161],"approaches":[163],"on":[164],"data.":[167]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3}],"updated_date":"2026-04-23T09:07:50.710637","created_date":"2025-10-10T00:00:00"}
