{"id":"https://openalex.org/W7133697705","doi":"https://doi.org/10.48550/arxiv.2603.03387","title":"Learning Order Forest for Qualitative-Attribute Data Clustering","display_name":"Learning Order Forest for Qualitative-Attribute Data Clustering","publication_year":2026,"publication_date":"2026-03-03","ids":{"openalex":"https://openalex.org/W7133697705","doi":"https://doi.org/10.48550/arxiv.2603.03387"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.03387","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.03387","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.03387","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5115028400","display_name":"Mingjie Zhao","orcid":"https://orcid.org/0009-0009-5517-4845"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Mingjie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015773958","display_name":"Sen Feng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Feng, Sen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128140032","display_name":"Yiqun Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Yiqun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055192762","display_name":"Mengke Li","orcid":"https://orcid.org/0000-0002-6373-7494"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Mengke","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128209818","display_name":"Yang Lu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu, Yang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5128191079","display_name":"Yiu-ming Cheung","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cheung, Yiu-ming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.6660000085830688,"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":0.6660000085830688,"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":0.030400000512599945,"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/T10057","display_name":"Face and Expression Recognition","score":0.02810000069439411,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.7017999887466431},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.5264999866485596},{"id":"https://openalex.org/keywords/euclidean-distance","display_name":"Euclidean distance","score":0.5120999813079834},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.45899999141693115},{"id":"https://openalex.org/keywords/euclidean-space","display_name":"Euclidean space","score":0.36500000953674316},{"id":"https://openalex.org/keywords/vertex","display_name":"Vertex (graph theory)","score":0.3610999882221222},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.3481000065803528},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3391999900341034}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7017999887466431},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.5264999866485596},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5141000151634216},{"id":"https://openalex.org/C120174047","wikidata":"https://www.wikidata.org/wiki/Q847073","display_name":"Euclidean distance","level":2,"score":0.5120999813079834},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4977000057697296},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4699000120162964},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.45899999141693115},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4375},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.38929998874664307},{"id":"https://openalex.org/C186450821","wikidata":"https://www.wikidata.org/wiki/Q17295","display_name":"Euclidean space","level":2,"score":0.36500000953674316},{"id":"https://openalex.org/C80899671","wikidata":"https://www.wikidata.org/wiki/Q1304193","display_name":"Vertex (graph theory)","level":3,"score":0.3610999882221222},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.3481000065803528},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3391999900341034},{"id":"https://openalex.org/C163797641","wikidata":"https://www.wikidata.org/wiki/Q2067937","display_name":"Tree structure","level":3,"score":0.3379000127315521},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.3321000039577484},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.3154999911785126},{"id":"https://openalex.org/C129782007","wikidata":"https://www.wikidata.org/wiki/Q162886","display_name":"Euclidean geometry","level":2,"score":0.3093000054359436},{"id":"https://openalex.org/C164866538","wikidata":"https://www.wikidata.org/wiki/Q367351","display_name":"Cluster (spacecraft)","level":2,"score":0.3043999969959259},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.2822999954223633},{"id":"https://openalex.org/C184509293","wikidata":"https://www.wikidata.org/wiki/Q5136711","display_name":"Clustering high-dimensional data","level":3,"score":0.26499998569488525},{"id":"https://openalex.org/C2776291640","wikidata":"https://www.wikidata.org/wiki/Q2912517","display_name":"Value (mathematics)","level":2,"score":0.25780001282691956}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.03387","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.03387","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.03387","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.03387","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Clustering":[0],"is":[1,15,20,99],"a":[2,48,66,92,95,126],"fundamental":[3],"approach":[4],"to":[5,52,75,101,143,147],"understanding":[6],"data":[7],"patterns,":[8],"wherein":[9],"the":[10,22,34,55,69,72,81,85,89,114,119,130,137,141,144,164,167],"intuitive":[11],"Euclidean":[12],"distance":[13,50,116],"space":[14,117],"commonly":[16],"adopted.":[17],"However,":[18],"this":[19],"not":[21],"case":[23],"for":[24],"implicit":[25],"cluster":[26],"distributions":[27],"reflected":[28],"by":[29,125],"qualitative":[30,61],"attribute":[31],"values,":[32],"e.g.,":[33],"nominal":[35],"values":[36],"of":[37,71,118,129,152,166],"attributes":[38],"like":[39],"symptoms,":[40],"marital":[41],"status,":[42],"etc.":[43],"This":[44],"paper,":[45],"therefore,":[46],"discovered":[47],"tree-like":[49],"structure":[51],"flexibly":[53],"represent":[54],"local":[56],"order":[57,78],"relationship":[58],"among":[59,80],"intra-attribute":[60],"values.":[62],"That":[63],"is,":[64],"treating":[65],"value":[67,83],"as":[68],"vertex":[70,82],"tree":[73,106],"allows":[74],"capture":[76],"rich":[77],"relationships":[79],"and":[84,108],"others.":[86],"To":[87],"obtain":[88,103],"trees":[90],"in":[91],"clustering-friendly":[93],"form,":[94],"joint":[96,138],"learning":[97,139],"mechanism":[98],"proposed":[100,168],"iteratively":[102],"more":[104],"appropriate":[105],"structures":[107],"clusters.":[109],"It":[110],"turns":[111],"out":[112],"that":[113,136],"latent":[115],"whole":[120],"dataset":[121],"can":[122],"be":[123],"well-represented":[124],"forest":[127,142],"consisting":[128],"learned":[131],"trees.":[132],"Extensive":[133],"experiments":[134],"demonstrate":[135],"adapts":[140],"clustering":[145],"task":[146],"yield":[148],"accurate":[149],"results.":[150],"Comparisons":[151],"10":[153],"counterparts":[154],"on":[155],"12":[156],"real":[157],"benchmark":[158],"datasets":[159],"with":[160],"significance":[161],"tests":[162],"verify":[163],"superiority":[165],"method.":[169]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-06T00:00:00"}
