{"id":"https://openalex.org/W1986441855","doi":"https://doi.org/10.1145/1458484.1458496","title":"Frequent pattern-growth approach for document organization","display_name":"Frequent pattern-growth approach for document organization","publication_year":2008,"publication_date":"2008-10-30","ids":{"openalex":"https://openalex.org/W1986441855","doi":"https://doi.org/10.1145/1458484.1458496","mag":"1986441855"},"language":"en","primary_location":{"id":"doi:10.1145/1458484.1458496","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1458484.1458496","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd international workshop on Ontologies and information systems for the semantic web","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/A5056726740","display_name":"Monika Akbar","orcid":"https://orcid.org/0000-0002-9402-5799"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Monika Akbar","raw_affiliation_strings":["Virginia Tech, Blacksburg, VA, USA","Virginia Tech, , Blacksburg, VA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Virginia Tech, Blacksburg, VA, USA","institution_ids":["https://openalex.org/I859038795"]},{"raw_affiliation_string":"Virginia Tech, , Blacksburg, VA, USA","institution_ids":["https://openalex.org/I859038795"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009847987","display_name":"Rafal A. Angryk","orcid":"https://orcid.org/0000-0001-9598-8207"},"institutions":[{"id":"https://openalex.org/I23732399","display_name":"Montana State University","ror":"https://ror.org/02w0trx84","country_code":"US","type":"education","lineage":["https://openalex.org/I23732399","https://openalex.org/I4210126032"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rafal A. Angryk","raw_affiliation_strings":["Montana State University, Bozeman, MT, USA","Montana State University , Bozeman , MT , USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Montana State University, Bozeman, MT, USA","institution_ids":["https://openalex.org/I23732399"]},{"raw_affiliation_string":"Montana State University , Bozeman , MT , USA","institution_ids":["https://openalex.org/I23732399"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.7608,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.67676129,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"77","last_page":"82"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9998999834060669,"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/T11106","display_name":"Data Management and Algorithms","score":0.9854999780654907,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.9742000102996826,"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/computer-science","display_name":"Computer science","score":0.7665094137191772},{"id":"https://openalex.org/keywords/document-clustering","display_name":"Document clustering","score":0.6866161227226257},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.6598060131072998},{"id":"https://openalex.org/keywords/hierarchy","display_name":"Hierarchy","score":0.5978140234947205},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5948427319526672},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5830814242362976},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5135388374328613},{"id":"https://openalex.org/keywords/association-rule-learning","display_name":"Association rule learning","score":0.49445152282714844},{"id":"https://openalex.org/keywords/mechanism","display_name":"Mechanism (biology)","score":0.43134254217147827},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.42704829573631287},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.23262089490890503},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.2067890465259552}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7665094137191772},{"id":"https://openalex.org/C177937566","wikidata":"https://www.wikidata.org/wiki/Q4223102","display_name":"Document clustering","level":3,"score":0.6866161227226257},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.6598060131072998},{"id":"https://openalex.org/C31170391","wikidata":"https://www.wikidata.org/wiki/Q188619","display_name":"Hierarchy","level":2,"score":0.5978140234947205},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5948427319526672},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5830814242362976},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5135388374328613},{"id":"https://openalex.org/C193524817","wikidata":"https://www.wikidata.org/wiki/Q386780","display_name":"Association rule learning","level":2,"score":0.49445152282714844},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.43134254217147827},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.42704829573631287},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.23262089490890503},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2067890465259552},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C34447519","wikidata":"https://www.wikidata.org/wiki/Q179522","display_name":"Market economy","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/1458484.1458496","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1458484.1458496","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd international workshop on Ontologies and information systems for the semantic web","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":24,"referenced_works":["https://openalex.org/W1484413656","https://openalex.org/W1493454437","https://openalex.org/W1506285740","https://openalex.org/W1565377632","https://openalex.org/W1570542661","https://openalex.org/W1924689489","https://openalex.org/W1938740620","https://openalex.org/W1986466096","https://openalex.org/W2064853889","https://openalex.org/W2092970194","https://openalex.org/W2110668502","https://openalex.org/W2140190241","https://openalex.org/W2156357380","https://openalex.org/W2166559705","https://openalex.org/W2168209541","https://openalex.org/W2170726034","https://openalex.org/W2186428165","https://openalex.org/W2482589566","https://openalex.org/W2999729612","https://openalex.org/W4252403066","https://openalex.org/W6633894697","https://openalex.org/W6634059332","https://openalex.org/W6680704940","https://openalex.org/W6685146747"],"related_works":["https://openalex.org/W2751920613","https://openalex.org/W2415164632","https://openalex.org/W2238349241","https://openalex.org/W2019737068","https://openalex.org/W2899601636","https://openalex.org/W4254379378","https://openalex.org/W3015674157","https://openalex.org/W4206655101","https://openalex.org/W4237592971","https://openalex.org/W2387982377"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3,38],"propose":[4],"a":[5,34,40,44,55],"document":[6,32],"clustering":[7],"mechanism":[8],"that":[9,51,99],"depends":[10],"on":[11,22,111],"the":[12,18,23,62,71,75,80,87,94,107,116],"appearance":[13],"of":[14,25,29,36,47,61,79,86,89],"frequent":[15,26,72,90],"senses":[16,113],"in":[17,84,93],"documents":[19,81,108],"rather":[20,114],"than":[21,115],"co-occurrence":[24],"keywords.":[27,118],"Instead":[28],"representing":[30],"each":[31],"as":[33],"collection":[35],"keywords,":[37],"use":[39],"document-graph":[41],"which":[42],"reflects":[43],"conceptual":[45],"hierarchy":[46],"keywords":[48],"related":[49],"to":[50,69,105],"document.":[52],"We":[53,97],"incorporate":[54],"graph":[56],"mining":[57,66],"approach":[58,102],"with":[59],"one":[60],"well-known":[63],"association":[64],"rule":[65],"procedures,":[67],"FP-growth,":[68],"discover":[70],"subgraphs":[73,91],"among":[74],"document-graphs.":[76,96],"The":[77],"similarity":[78],"is":[82],"measured":[83],"terms":[85],"number":[88],"appearing":[92],"corresponding":[95],"believe":[98],"our":[100],"novel":[101],"allows":[103],"us":[104],"cluster":[106],"based":[109],"more":[110],"their":[112],"actual":[117]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2},{"year":2014,"cited_by_count":3},{"year":2012,"cited_by_count":3}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}