{"id":"https://openalex.org/W2626924204","doi":"https://doi.org/10.1145/3055635.3056580","title":"Similarity Measure Based on Adaptive Neighbors for Spectral Clustering","display_name":"Similarity Measure Based on Adaptive Neighbors for Spectral Clustering","publication_year":2017,"publication_date":"2017-02-24","ids":{"openalex":"https://openalex.org/W2626924204","doi":"https://doi.org/10.1145/3055635.3056580","mag":"2626924204"},"language":"en","primary_location":{"id":"doi:10.1145/3055635.3056580","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3055635.3056580","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 9th International Conference on Machine Learning and Computing","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/A5063256241","display_name":"Xiucai Ye","orcid":"https://orcid.org/0000-0002-5547-3919"},"institutions":[{"id":"https://openalex.org/I146399215","display_name":"University of Tsukuba","ror":"https://ror.org/02956yf07","country_code":"JP","type":"education","lineage":["https://openalex.org/I146399215"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Xiucai Ye","raw_affiliation_strings":["University of Tsukuba, Tsukuba City, Ibaraki, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Tsukuba, Tsukuba City, Ibaraki, Japan","institution_ids":["https://openalex.org/I146399215"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038336830","display_name":"Tetsuya Sakurai","orcid":"https://orcid.org/0000-0002-5789-7547"},"institutions":[{"id":"https://openalex.org/I146399215","display_name":"University of Tsukuba","ror":"https://ror.org/02956yf07","country_code":"JP","type":"education","lineage":["https://openalex.org/I146399215"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tetsuya Sakurai","raw_affiliation_strings":["University of Tsukuba, Tsukuba City, Ibaraki, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Tsukuba, Tsukuba City, Ibaraki, Japan","institution_ids":["https://openalex.org/I146399215"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I146399215"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"405","last_page":"409"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9970999956130981,"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"}},"topics":[{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9970999956130981,"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"}},{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.995199978351593,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9944999814033508,"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/spectral-clustering","display_name":"Spectral clustering","score":0.8729289770126343},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.8710794448852539},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.7063854932785034},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6463451385498047},{"id":"https://openalex.org/keywords/similarity-measure","display_name":"Similarity measure","score":0.6254664659500122},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6010482907295227},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.6003812551498413},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.5788065195083618},{"id":"https://openalex.org/keywords/cure-data-clustering-algorithm","display_name":"CURE data clustering algorithm","score":0.5665647387504578},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5221576690673828},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.5206941366195679},{"id":"https://openalex.org/keywords/single-linkage-clustering","display_name":"Single-linkage clustering","score":0.47886034846305847},{"id":"https://openalex.org/keywords/clustering-high-dimensional-data","display_name":"Clustering high-dimensional data","score":0.46213001012802124},{"id":"https://openalex.org/keywords/fuzzy-clustering","display_name":"Fuzzy clustering","score":0.46201080083847046},{"id":"https://openalex.org/keywords/consensus-clustering","display_name":"Consensus clustering","score":0.4579206705093384},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3218345642089844},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.06446939706802368}],"concepts":[{"id":"https://openalex.org/C105611402","wikidata":"https://www.wikidata.org/wiki/Q2976589","display_name":"Spectral clustering","level":3,"score":0.8729289770126343},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.8710794448852539},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.7063854932785034},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6463451385498047},{"id":"https://openalex.org/C2776517306","wikidata":"https://www.wikidata.org/wiki/Q29017317","display_name":"Similarity measure","level":2,"score":0.6254664659500122},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6010482907295227},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6003812551498413},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.5788065195083618},{"id":"https://openalex.org/C33704608","wikidata":"https://www.wikidata.org/wiki/Q5014717","display_name":"CURE data clustering algorithm","level":4,"score":0.5665647387504578},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5221576690673828},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.5206941366195679},{"id":"https://openalex.org/C22648726","wikidata":"https://www.wikidata.org/wiki/Q7523744","display_name":"Single-linkage clustering","level":5,"score":0.47886034846305847},{"id":"https://openalex.org/C184509293","wikidata":"https://www.wikidata.org/wiki/Q5136711","display_name":"Clustering high-dimensional data","level":3,"score":0.46213001012802124},{"id":"https://openalex.org/C17212007","wikidata":"https://www.wikidata.org/wiki/Q5511111","display_name":"Fuzzy clustering","level":3,"score":0.46201080083847046},{"id":"https://openalex.org/C186767784","wikidata":"https://www.wikidata.org/wiki/Q5162841","display_name":"Consensus clustering","level":5,"score":0.4579206705093384},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3218345642089844},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.06446939706802368}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3055635.3056580","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3055635.3056580","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 9th International Conference on Machine Learning and Computing","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":22,"referenced_works":["https://openalex.org/W1549123574","https://openalex.org/W1666782210","https://openalex.org/W1966461475","https://openalex.org/W1979089718","https://openalex.org/W1997996331","https://openalex.org/W2003772934","https://openalex.org/W2052005199","https://openalex.org/W2073161435","https://openalex.org/W2096044434","https://openalex.org/W2103560185","https://openalex.org/W2121947440","https://openalex.org/W2132914434","https://openalex.org/W2141465109","https://openalex.org/W2152322845","https://openalex.org/W2154642173","https://openalex.org/W2165874743","https://openalex.org/W2296319761","https://openalex.org/W2342032814","https://openalex.org/W4250183413","https://openalex.org/W4250589301","https://openalex.org/W4254300870","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W2003772934","https://openalex.org/W2131828344","https://openalex.org/W2287240295","https://openalex.org/W2199594781","https://openalex.org/W2187382873","https://openalex.org/W2953854373","https://openalex.org/W4229037312","https://openalex.org/W2038937869","https://openalex.org/W2965089876","https://openalex.org/W1856136452"],"abstract_inverted_index":{"Spectral":[0],"clustering":[1,9,43,131],"has":[2],"become":[3],"one":[4],"of":[5,22,51,61],"the":[6,20,39,46,56,69,85,96,103,117,128],"most":[7],"popular":[8],"methods":[10],"for":[11,41],"exploratory":[12],"data":[13,53,63,74,90],"analysis.":[14],"Similarity":[15],"measure":[16],"is":[17,65,81,93],"crucial":[18],"to":[19,28,83,95],"performance":[21],"spectral":[23,30,42,130],"clustering.":[24],"In":[25],"this":[26],"paper,":[27],"improve":[29],"clustering,":[31],"we":[32],"propose":[33],"an":[34],"efficient":[35],"method":[36,80,105,119],"that":[37,71,116],"measures":[38],"similarity":[40,60,87],"by":[44,106],"considering":[45],"adaptive":[47],"and":[48,92],"optimal":[49],"neighbors":[50],"each":[52],"based":[54,67],"on":[55,68],"local":[57],"structure.":[58],"The":[59,78,112],"two":[62,73],"points":[64,75],"measured":[66],"probability":[70],"these":[72],"are":[76],"neighbors.":[77],"proposed":[79,104,118],"able":[82],"explore":[84],"underlying":[86],"relationships":[88],"between":[89],"points,":[91],"robust":[94],"datasets":[97],"with":[98],"high":[99,108],"dimensions.":[100],"We":[101],"evaluate":[102],"using":[107],"dimensional":[109],"real-world":[110],"datasets.":[111],"experimental":[113],"results":[114],"demonstrate":[115],"not":[120],"only":[121],"achieves":[122],"good":[123],"performance,":[124],"but":[125],"also":[126],"outperforms":[127],"traditional":[129],"algorithms.":[132]},"counts_by_year":[{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
