{"id":"https://openalex.org/W3216631516","doi":null,"title":"A high-order affinity propagation clustering algorithm based on tensor distance","display_name":"A high-order affinity propagation clustering algorithm based on tensor distance","publication_year":2016,"publication_date":"2016-01-01","ids":{"openalex":"https://openalex.org/W3216631516","mag":"3216631516"},"language":"en","primary_location":{"id":"mag:3216631516","doi":null,"is_oa":false,"landing_page_url":"http://open.oriprobe.com/articles/47782947/A_high_order_affinity_propagation_clustering_algor.htm","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"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/A5100621869","display_name":"Xuan Yan","orcid":"https://orcid.org/0009-0003-1320-351X","observed_orcids":["https://orcid.org/0009-0003-1320-351X"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xuan Yan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5073859287","display_name":"Zhou Chuan-sheng","orcid":"https://orcid.org/0009-0006-0410-2639","observed_orcids":["https://orcid.org/0009-0006-0410-2639"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou Chuan-sheng","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":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.34721334,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":"1","first_page":"96","last_page":"99"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","score":1.0,"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":1.0,"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/T13373","display_name":"Data Mining and Machine Learning Applications","score":0.0,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.0,"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/affinity-propagation","display_name":"affinity propagation","score":0.9679999947547913},{"id":"https://openalex.org/keywords/similarity-calculation","display_name":"similarity measurement","score":0.5830000042915344},{"id":"https://openalex.org/keywords/automatic-determination-of-number-of-clusters","display_name":"automatic determination of number of clusters","score":0.7310000061988831}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.8800208568572998},{"id":"https://openalex.org/C109659709","wikidata":"https://www.wikidata.org/wiki/Q3407504","display_name":"Affinity propagation","level":5,"score":0.7843327522277832},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.7284514904022217},{"id":"https://openalex.org/C33704608","wikidata":"https://www.wikidata.org/wiki/Q5014717","display_name":"CURE data clustering algorithm","level":4,"score":0.7108978629112244},{"id":"https://openalex.org/C193143536","wikidata":"https://www.wikidata.org/wiki/Q5227360","display_name":"Data stream clustering","level":5,"score":0.6653521656990051},{"id":"https://openalex.org/C17212007","wikidata":"https://www.wikidata.org/wiki/Q5511111","display_name":"Fuzzy clustering","level":3,"score":0.5926138758659363},{"id":"https://openalex.org/C104047586","wikidata":"https://www.wikidata.org/wiki/Q5033439","display_name":"Canopy clustering algorithm","level":4,"score":0.5787053108215332},{"id":"https://openalex.org/C22648726","wikidata":"https://www.wikidata.org/wiki/Q7523744","display_name":"Single-linkage clustering","level":5,"score":0.5661928057670593},{"id":"https://openalex.org/C115328559","wikidata":"https://www.wikidata.org/wiki/Q4041956","display_name":"k-medians clustering","level":5,"score":0.5253476500511169},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.49793410301208496},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4911665618419647},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.47965624928474426},{"id":"https://openalex.org/C27964816","wikidata":"https://www.wikidata.org/wiki/Q5164359","display_name":"Constrained clustering","level":5,"score":0.46740370988845825},{"id":"https://openalex.org/C23822008","wikidata":"https://www.wikidata.org/wiki/Q5156437","display_name":"Complete-linkage clustering","level":5,"score":0.4444708228111267},{"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":0.4440988004207611},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4397132992744446},{"id":"https://openalex.org/C184509293","wikidata":"https://www.wikidata.org/wiki/Q5136711","display_name":"Clustering high-dimensional data","level":3,"score":0.4199722409248352},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3880573809146881},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2772422730922699},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:3216631516","doi":null,"is_oa":false,"landing_page_url":"http://open.oriprobe.com/articles/47782947/A_high_order_affinity_propagation_clustering_algor.htm","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7208265628","https://openalex.org/W4401596674","https://openalex.org/W4397033664","https://openalex.org/W1975195692","https://openalex.org/W2558362515","https://openalex.org/W2370909876","https://openalex.org/W2356494833","https://openalex.org/W4312058513","https://openalex.org/W4388481433","https://openalex.org/W2610223144"],"abstract_inverted_index":{"Affinity":[0],"propagation(AP)algorithm":[1],"does":[2],"not":[3],"need":[4],"to":[5,63,98],"specify":[6],"the":[7,12,17,21,25,92,95,100,109,113,118,123,126,130,134,137,142],"number":[8,22],"of":[9,23,30,52,112,125],"clustering.When":[10],"running":[11],"program,it":[13],"can":[14,43,107],"automatically":[15],"identify":[16],"clustering":[18,32,41,46,81],"center":[19],"and":[20,36,132],"clustering.On":[24],"same":[26],"data":[27,65,89,115,149],"set,the":[28],"result":[29],"AP":[31],"algorithm":[33,42,59,76,85],"is":[34,145],"stable":[35],"has":[37],"good":[38],"robustness.In":[39],"addition,AP":[40],"get":[44],"accurate":[45],"results":[47,140],"by":[48,91,121],"using":[49],"a":[50,72],"variety":[51],"distance":[53,97,106,124],"measuring":[54],"methods.But":[55],"current":[56],"affinity":[57,74],"propagation":[58,75],"cannot":[60],"be":[61],"applied":[62],"heterogeneous":[64,82,88,114,148],"clustering.Aiming":[66],"at":[67],"this":[68],"problem,the":[69],"paper":[70],"proposes":[71],"high-order":[73,119],"based":[77],"on":[78],"tensor":[79,96,105],"distance(HTDAP)for":[80],"data.The":[83],"proposed":[84,143],"represents":[86],"each":[87],"object":[90],"tensor,and":[93],"introduces":[94],"measure":[99,133],"similarity":[101],"between":[102,129],"two":[103],"objects.The":[104],"capture":[108],"distribution":[110],"features":[111],"sets":[116],"in":[117,147],"space":[120],"calculating":[122],"numerical":[127],"values":[128],"objects":[131],"difference":[135],"among":[136],"coordinate":[138],"positions.Experimental":[139],"show":[141],"scheme":[144],"effective":[146],"clustering.":[150]},"counts_by_year":[],"updated_date":"2025-11-06T04:12:42.849631","created_date":"2021-12-06T00:00:00"}
