{"id":"https://openalex.org/W2082691776","doi":"https://doi.org/10.1080/00207720600891620","title":"ICLUS: A robust and scalable clustering model for time series via independent component analysis","display_name":"ICLUS: A robust and scalable clustering model for time series via independent component analysis","publication_year":2006,"publication_date":"2006-09-12","ids":{"openalex":"https://openalex.org/W2082691776","doi":"https://doi.org/10.1080/00207720600891620","mag":"2082691776"},"language":"en","primary_location":{"id":"doi:10.1080/00207720600891620","is_oa":false,"landing_page_url":"https://doi.org/10.1080/00207720600891620","pdf_url":null,"source":{"id":"https://openalex.org/S129640837","display_name":"International Journal of Systems Science","issn_l":"0020-7721","issn":["0020-7721","1464-5319"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Systems Science","raw_type":"journal-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/A5032306211","display_name":"Edmond H. C. Wu","orcid":"https://orcid.org/0000-0002-9286-5903"},"institutions":[{"id":"https://openalex.org/I889458895","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86","country_code":"HK","type":"education","lineage":["https://openalex.org/I889458895"]}],"countries":["HK"],"is_corresponding":true,"raw_author_name":"Edmond H. C. Wu","raw_affiliation_strings":["The University of Hong Kong","Department of Statistics and Actuarial Science , The University of Hong Kong , Pokfulam Road , Hong Kong"],"affiliations":[{"raw_affiliation_string":"The University of Hong Kong","institution_ids":["https://openalex.org/I889458895"]},{"raw_affiliation_string":"Department of Statistics and Actuarial Science , The University of Hong Kong , Pokfulam Road , Hong Kong","institution_ids":["https://openalex.org/I889458895"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018396769","display_name":"Philip L. H. Yu","orcid":"https://orcid.org/0000-0002-9449-0420"},"institutions":[{"id":"https://openalex.org/I889458895","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86","country_code":"HK","type":"education","lineage":["https://openalex.org/I889458895"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Philip L. H. Yu","raw_affiliation_strings":["The University of Hong Kong","Department of Statistics and Actuarial Science , The University of Hong Kong , Pokfulam Road , Hong Kong"],"affiliations":[{"raw_affiliation_string":"The University of Hong Kong","institution_ids":["https://openalex.org/I889458895"]},{"raw_affiliation_string":"Department of Statistics and Actuarial Science , The University of Hong Kong , Pokfulam Road , Hong Kong","institution_ids":["https://openalex.org/I889458895"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5032306211"],"corresponding_institution_ids":["https://openalex.org/I889458895"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.14392551,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"37","issue":"13","first_page":"987","last_page":"1001"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9994000196456909,"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"}},"topics":[{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9994000196456909,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9916999936103821,"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/T11270","display_name":"Complex Systems and Time Series Analysis","score":0.9914000034332275,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.8104404211044312},{"id":"https://openalex.org/keywords/independent-component-analysis","display_name":"Independent component analysis","score":0.737954318523407},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6148166656494141},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5844731330871582},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5599479079246521},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.5581738948822021},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.4807872176170349},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4581441581249237},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4465029537677765},{"id":"https://openalex.org/keywords/cure-data-clustering-algorithm","display_name":"CURE data clustering algorithm","score":0.4406737685203552},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.42982572317123413},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.4145074188709259},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.380243718624115},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.356563925743103},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2692131996154785},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.20648810267448425}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.8104404211044312},{"id":"https://openalex.org/C51432778","wikidata":"https://www.wikidata.org/wiki/Q1259145","display_name":"Independent component analysis","level":2,"score":0.737954318523407},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6148166656494141},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5844731330871582},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5599479079246521},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.5581738948822021},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.4807872176170349},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4581441581249237},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4465029537677765},{"id":"https://openalex.org/C33704608","wikidata":"https://www.wikidata.org/wiki/Q5014717","display_name":"CURE data clustering algorithm","level":4,"score":0.4406737685203552},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.42982572317123413},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.4145074188709259},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.380243718624115},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.356563925743103},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2692131996154785},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.20648810267448425},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/00207720600891620","is_oa":false,"landing_page_url":"https://doi.org/10.1080/00207720600891620","pdf_url":null,"source":{"id":"https://openalex.org/S129640837","display_name":"International Journal of Systems Science","issn_l":"0020-7721","issn":["0020-7721","1464-5319"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Systems Science","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W32788597","https://openalex.org/W57842594","https://openalex.org/W151863654","https://openalex.org/W1493454437","https://openalex.org/W1507743750","https://openalex.org/W1523767002","https://openalex.org/W1530790987","https://openalex.org/W1546119576","https://openalex.org/W1548802052","https://openalex.org/W1551787908","https://openalex.org/W1566114229","https://openalex.org/W1584005513","https://openalex.org/W1673310716","https://openalex.org/W1806663855","https://openalex.org/W1963737619","https://openalex.org/W1972228597","https://openalex.org/W1977496278","https://openalex.org/W1978572064","https://openalex.org/W1995463878","https://openalex.org/W1996355918","https://openalex.org/W1996794795","https://openalex.org/W2002016209","https://openalex.org/W2006783944","https://openalex.org/W2012627916","https://openalex.org/W2019502123","https://openalex.org/W2026711836","https://openalex.org/W2044625360","https://openalex.org/W2088698696","https://openalex.org/W2099741732","https://openalex.org/W2100718094","https://openalex.org/W2127218421","https://openalex.org/W2131687179","https://openalex.org/W2138144286","https://openalex.org/W2139754543","https://openalex.org/W2141224535","https://openalex.org/W2149230623","https://openalex.org/W2548040281","https://openalex.org/W2999729612","https://openalex.org/W3003734944","https://openalex.org/W3121343760","https://openalex.org/W4205778870","https://openalex.org/W4244161474"],"related_works":["https://openalex.org/W2559422900","https://openalex.org/W2171610853","https://openalex.org/W3144143113","https://openalex.org/W3022637481","https://openalex.org/W2491448268","https://openalex.org/W3120229345","https://openalex.org/W2892323093","https://openalex.org/W2394117789","https://openalex.org/W2160785859","https://openalex.org/W2390610678"],"abstract_inverted_index":{"As":[0],"a":[1,22,50,95],"statistical":[2],"technique,":[3],"independent":[4,17,44],"component":[5],"analysis":[6],"(ICA)":[7],"is":[8,20,65,77,121],"used":[9,105],"to":[10,37,55,60,67,106],"separate":[11],"mixed":[12],"data":[13,29,42,91],"sources":[14],"into":[15,43],"statistically":[16],"patterns.":[18,114],"ICA":[19,36],"also":[21,78,126],"useful":[23],"dimension":[24],"reduction":[25],"technique":[26],"for":[27],"multivariate":[28,39],"analysis.":[30],"In":[31],"this":[32,119],"article,":[33],"we":[34],"apply":[35],"transform":[38],"time":[40,57,89,109],"series":[41,58,90,110],"components":[45],"(ICs),":[46],"and":[47,70,123],"then":[48],"develop":[49],"clustering":[51,87,101,131],"algorithm":[52],"called":[53],"ICLUS":[54,64],"group":[56],"according":[59],"the":[61,74],"ICs":[62],"found.":[63],"robust":[66],"noises,":[68],"outliers,":[69],"different":[71],"scales":[72],"in":[73,86],"data.":[75],"It":[76],"scalable":[79],"because":[80],"it":[81],"can":[82,103],"achieve":[83],"satisfactory":[84],"performance":[85],"large":[88],"sets":[92],"based":[93],"on":[94],"modest":[96],"number":[97],"of":[98],"ICs.":[99],"The":[100,115],"model":[102],"be":[104],"cluster":[107],"financial":[108],"with":[111],"similar":[112],"structural":[113],"experiments":[116],"show":[117],"that":[118],"method":[120],"effective":[122],"efficient,":[124],"which":[125],"significantly":[127],"outperforms":[128],"other":[129],"comparable":[130],"methods,":[132],"such":[133],"as":[134],"distance-based":[135],"approaches.":[136]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2026-03-06T06:45:51.903784","created_date":"2025-10-10T00:00:00"}
