{"id":"https://openalex.org/W4391346495","doi":"https://doi.org/10.3390/a17020061","title":"Efficient Time-Series Clustering through Sparse Gaussian Modeling","display_name":"Efficient Time-Series Clustering through Sparse Gaussian Modeling","publication_year":2024,"publication_date":"2024-01-30","ids":{"openalex":"https://openalex.org/W4391346495","doi":"https://doi.org/10.3390/a17020061"},"language":"en","primary_location":{"id":"doi:10.3390/a17020061","is_oa":true,"landing_page_url":"https://doi.org/10.3390/a17020061","pdf_url":"https://www.mdpi.com/1999-4893/17/2/61/pdf?version=1706625710","source":{"id":"https://openalex.org/S190629608","display_name":"Algorithms","issn_l":"1999-4893","issn":["1999-4893"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Algorithms","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1999-4893/17/2/61/pdf?version=1706625710","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5021246308","display_name":"Dimitris Fotakis","orcid":"https://orcid.org/0000-0001-6864-8960"},"institutions":[{"id":"https://openalex.org/I174458059","display_name":"National Technical University of Athens","ror":"https://ror.org/03cx6bg69","country_code":"GR","type":"education","lineage":["https://openalex.org/I174458059"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Dimitris Fotakis","raw_affiliation_strings":["School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Zografou, Greece"],"raw_orcid":"https://orcid.org/0000-0001-6864-8960","affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Zografou, Greece","institution_ids":["https://openalex.org/I174458059"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084616236","display_name":"Panagiotis Patsilinakos","orcid":null},"institutions":[{"id":"https://openalex.org/I174458059","display_name":"National Technical University of Athens","ror":"https://ror.org/03cx6bg69","country_code":"GR","type":"education","lineage":["https://openalex.org/I174458059"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Panagiotis Patsilinakos","raw_affiliation_strings":["School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Zografou, Greece"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Zografou, Greece","institution_ids":["https://openalex.org/I174458059"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043416420","display_name":"Eleni Psaroudaki","orcid":"https://orcid.org/0000-0003-4668-9029"},"institutions":[{"id":"https://openalex.org/I174458059","display_name":"National Technical University of Athens","ror":"https://ror.org/03cx6bg69","country_code":"GR","type":"education","lineage":["https://openalex.org/I174458059"]}],"countries":["GR"],"is_corresponding":true,"raw_author_name":"Eleni Psaroudaki","raw_affiliation_strings":["School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Zografou, Greece"],"raw_orcid":"https://orcid.org/0000-0003-4668-9029","affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Zografou, Greece","institution_ids":["https://openalex.org/I174458059"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034032956","display_name":"Michalis Xefteris","orcid":null},"institutions":[{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I39804081","display_name":"Sorbonne Universit\u00e9","ror":"https://ror.org/02en5vm52","country_code":"FR","type":"education","lineage":["https://openalex.org/I39804081"]},{"id":"https://openalex.org/I4210159731","display_name":"LIP6","ror":"https://ror.org/05krcen59","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I1294671590","https://openalex.org/I39804081","https://openalex.org/I4210159245","https://openalex.org/I4210159731"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Michalis Xefteris","raw_affiliation_strings":["LIP6, Sorbonne Universit\u00e9, CNRS, F-75005 Paris, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LIP6, Sorbonne Universit\u00e9, CNRS, F-75005 Paris, France","institution_ids":["https://openalex.org/I1294671590","https://openalex.org/I39804081","https://openalex.org/I4210159731"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5043416420"],"corresponding_institution_ids":["https://openalex.org/I174458059"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1782},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1782},"fwci":1.0764,"has_fulltext":true,"cited_by_count":5,"citation_normalized_percentile":{"value":0.73046154,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"17","issue":"2","first_page":"61","last_page":"61"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":1.0,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":1.0,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9934999942779541,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9869999885559082,"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/dynamic-time-warping","display_name":"Dynamic time warping","score":0.73233562707901},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.720107913017273},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5824910402297974},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5605587959289551},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5536295175552368},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.5327178239822388},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.5114880800247192},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4440319240093231},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4269405007362366},{"id":"https://openalex.org/keywords/cure-data-clustering-algorithm","display_name":"CURE data clustering algorithm","score":0.42670321464538574},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.418676495552063},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4060797393321991},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3916815519332886},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.3823443353176117},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3563666045665741},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.18249014019966125}],"concepts":[{"id":"https://openalex.org/C88516994","wikidata":"https://www.wikidata.org/wiki/Q1268863","display_name":"Dynamic time warping","level":2,"score":0.73233562707901},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.720107913017273},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5824910402297974},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5605587959289551},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5536295175552368},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.5327178239822388},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.5114880800247192},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4440319240093231},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4269405007362366},{"id":"https://openalex.org/C33704608","wikidata":"https://www.wikidata.org/wiki/Q5014717","display_name":"CURE data clustering algorithm","level":4,"score":0.42670321464538574},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.418676495552063},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4060797393321991},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3916815519332886},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.3823443353176117},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3563666045665741},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.18249014019966125},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/a17020061","is_oa":true,"landing_page_url":"https://doi.org/10.3390/a17020061","pdf_url":"https://www.mdpi.com/1999-4893/17/2/61/pdf?version=1706625710","source":{"id":"https://openalex.org/S190629608","display_name":"Algorithms","issn_l":"1999-4893","issn":["1999-4893"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Algorithms","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:7308feacf1a24f1cb1f39000060ced0b","is_oa":true,"landing_page_url":"https://doaj.org/article/7308feacf1a24f1cb1f39000060ced0b","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Algorithms, Vol 17, Iss 2, p 61 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/a17020061","is_oa":true,"landing_page_url":"https://doi.org/10.3390/a17020061","pdf_url":"https://www.mdpi.com/1999-4893/17/2/61/pdf?version=1706625710","source":{"id":"https://openalex.org/S190629608","display_name":"Algorithms","issn_l":"1999-4893","issn":["1999-4893"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Algorithms","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3382089774","display_name":null,"funder_award_id":"HFRI-FM17-1424","funder_id":"https://openalex.org/F4320327859","funder_display_name":"Hellenic Foundation for Research and Innovation"}],"funders":[{"id":"https://openalex.org/F4320323149","display_name":"National and Kapodistrian University of Athens","ror":"https://ror.org/04gnjpq42"},{"id":"https://openalex.org/F4320327859","display_name":"Hellenic Foundation for Research and Innovation","ror":null},{"id":"https://openalex.org/F4320330142","display_name":"National Technical University of Athens","ror":"https://ror.org/03cx6bg69"}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4391346495.pdf"},"referenced_works_count":36,"referenced_works":["https://openalex.org/W137285897","https://openalex.org/W848133049","https://openalex.org/W1894414046","https://openalex.org/W1983838190","https://openalex.org/W2001670934","https://openalex.org/W2033403400","https://openalex.org/W2049346399","https://openalex.org/W2051434435","https://openalex.org/W2084616221","https://openalex.org/W2097747115","https://openalex.org/W2099302229","https://openalex.org/W2106595237","https://openalex.org/W2113054345","https://openalex.org/W2129564505","https://openalex.org/W2163336863","https://openalex.org/W2239813812","https://openalex.org/W2342476492","https://openalex.org/W2398004122","https://openalex.org/W2476349399","https://openalex.org/W2571670780","https://openalex.org/W2622816133","https://openalex.org/W2893995718","https://openalex.org/W2952414688","https://openalex.org/W2970658101","https://openalex.org/W3039352388","https://openalex.org/W3047381018","https://openalex.org/W3126626577","https://openalex.org/W3147178137","https://openalex.org/W4211049957","https://openalex.org/W4235169531","https://openalex.org/W4239867545","https://openalex.org/W4389763759","https://openalex.org/W6712328107","https://openalex.org/W6758804334","https://openalex.org/W6779988842","https://openalex.org/W6781770815"],"related_works":["https://openalex.org/W2559422900","https://openalex.org/W2171610853","https://openalex.org/W3144143113","https://openalex.org/W3022637481","https://openalex.org/W3120229345","https://openalex.org/W2491448268","https://openalex.org/W2394117789","https://openalex.org/W2160785859","https://openalex.org/W2390610678","https://openalex.org/W2394193399"],"abstract_inverted_index":{"In":[0,31,66],"this":[1],"work,":[2],"we":[3,35,70],"consider":[4],"the":[5,12,32,51,57,67,79,90,120,127,133,153,166,173,187,191],"problem":[6],"of":[7,46,62,85,100,109,123,136,152,175],"shape-based":[8],"time-series":[9],"clustering":[10,176,188],"with":[11,53,73],"widely":[13],"used":[14],"Dynamic":[15],"Time":[16],"Warping":[17],"(DTW)":[18],"distance.":[19],"We":[20,157],"present":[21],"a":[22,43,54,83,98],"novel":[23],"two-stage":[24],"framework":[25,114,183],"based":[26],"on":[27],"Sparse":[28,37,111,180],"Gaussian":[29,38],"Modeling.":[30],"first":[33],"stage,":[34,69],"apply":[36,71],"Process":[39],"Regression":[40],"and":[41,148],"obtain":[42],"sparse":[44],"representation":[45],"each":[47,93],"time":[48,108,122,135,155],"series":[49],"in":[50],"dataset":[52,81,129],"logarithmic":[55],"(in":[56],"original":[58,102,128,154],"length":[59],"T)":[60],"number":[61],"inducing":[63,94],"data":[64,103],"points.":[65,104],"second":[68],"k-means":[72,125,193],"DTW":[74,137],"Barycentric":[75],"Averaging":[76],"(DBA)":[77],"to":[78,126,140,145,186],"sparsified":[80],"using":[82,163],"generalization":[84],"DTW,":[86],"which":[87],"accounts":[88],"for":[89],"fact":[91],"that":[92,172],"point":[95],"serves":[96],"as":[97],"representative":[99],"many":[101],"The":[105],"asymptotic":[106],"running":[107,121,134],"our":[110,179],"Time-Series":[112,168,181],"Clustering":[113,182],"is":[115,184],"\u03a9(T2/log2T)":[116],"times":[117],"faster":[118],"than":[119],"applying":[124],"because":[130],"sparsification":[131,143],"reduces":[132],"from":[138,165],"\u0398(T2)":[139],"\u0398(log2T).":[141],"Moreover,":[142],"tends":[144],"smoothen":[146],"outliers":[147],"particularly":[149],"noisy":[150],"parts":[151],"series.":[156],"conduct":[158],"an":[159],"extensive":[160],"experimental":[161],"evaluation":[162],"datasets":[164],"UCR":[167],"Classification":[169],"Archive,":[170],"showing":[171],"quality":[174],"computed":[177,189],"by":[178,190],"comparable":[185],"standard":[192],"algorithm.":[194]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}