{"id":"https://openalex.org/W2069649755","doi":"https://doi.org/10.1109/isda.2011.6121776","title":"Analysis of spectral clustering algorithms for linear and nonlinear time series","display_name":"Analysis of spectral clustering algorithms for linear and nonlinear time series","publication_year":2011,"publication_date":"2011-11-01","ids":{"openalex":"https://openalex.org/W2069649755","doi":"https://doi.org/10.1109/isda.2011.6121776","mag":"2069649755"},"language":"en","primary_location":{"id":"doi:10.1109/isda.2011.6121776","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isda.2011.6121776","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2011 11th International Conference on Intelligent Systems Design and Applications","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/A5032964352","display_name":"Mauro Tucci","orcid":"https://orcid.org/0000-0002-5969-5455"},"institutions":[{"id":"https://openalex.org/I108290504","display_name":"University of Pisa","ror":"https://ror.org/03ad39j10","country_code":"IT","type":"education","lineage":["https://openalex.org/I108290504"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Mauro Tucci","raw_affiliation_strings":["Department of Energy and Systems Engineering, University of Pisa, Pisa, Italy","[Department of Energy and Systems Engineering, University of Pisa, Pisa, Italy]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Energy and Systems Engineering, University of Pisa, Pisa, Italy","institution_ids":["https://openalex.org/I108290504"]},{"raw_affiliation_string":"[Department of Energy and Systems Engineering, University of Pisa, Pisa, Italy]","institution_ids":["https://openalex.org/I108290504"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010643307","display_name":"Marco Raugi","orcid":"https://orcid.org/0000-0001-6231-6690"},"institutions":[{"id":"https://openalex.org/I108290504","display_name":"University of Pisa","ror":"https://ror.org/03ad39j10","country_code":"IT","type":"education","lineage":["https://openalex.org/I108290504"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Marco Raugi","raw_affiliation_strings":["Department of Energy and Systems Engineering, University of Pisa, Pisa, Italy","[Department of Energy and Systems Engineering, University of Pisa, Pisa, Italy]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Energy and Systems Engineering, University of Pisa, Pisa, Italy","institution_ids":["https://openalex.org/I108290504"]},{"raw_affiliation_string":"[Department of Energy and Systems Engineering, University of Pisa, Pisa, Italy]","institution_ids":["https://openalex.org/I108290504"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I108290504"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.14299706,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"925","last_page":"930"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9979000091552734,"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":0.9979000091552734,"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":0.9890000224113464,"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"}},{"id":"https://openalex.org/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9688000082969666,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7430134415626526},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.7172931432723999},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.617926299571991},{"id":"https://openalex.org/keywords/autoregressive\u2013moving-average-model","display_name":"Autoregressive\u2013moving-average model","score":0.6033620834350586},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.578644871711731},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.5772526264190674},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5769374966621399},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.5417339205741882},{"id":"https://openalex.org/keywords/spectral-clustering","display_name":"Spectral clustering","score":0.5126858949661255},{"id":"https://openalex.org/keywords/heteroscedasticity","display_name":"Heteroscedasticity","score":0.4762194752693176},{"id":"https://openalex.org/keywords/rand-index","display_name":"Rand index","score":0.4686272442340851},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.424551784992218},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3621429204940796},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.2921420931816101},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.25003355741500854},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.13637328147888184},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.13523173332214355}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7430134415626526},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.7172931432723999},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.617926299571991},{"id":"https://openalex.org/C74883015","wikidata":"https://www.wikidata.org/wiki/Q290467","display_name":"Autoregressive\u2013moving-average model","level":3,"score":0.6033620834350586},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.578644871711731},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.5772526264190674},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5769374966621399},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.5417339205741882},{"id":"https://openalex.org/C105611402","wikidata":"https://www.wikidata.org/wiki/Q2976589","display_name":"Spectral clustering","level":3,"score":0.5126858949661255},{"id":"https://openalex.org/C101104100","wikidata":"https://www.wikidata.org/wiki/Q1063540","display_name":"Heteroscedasticity","level":2,"score":0.4762194752693176},{"id":"https://openalex.org/C111442797","wikidata":"https://www.wikidata.org/wiki/Q7291446","display_name":"Rand index","level":3,"score":0.4686272442340851},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.424551784992218},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3621429204940796},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2921420931816101},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.25003355741500854},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.13637328147888184},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.13523173332214355},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/isda.2011.6121776","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isda.2011.6121776","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2011 11th International Conference on Intelligent Systems Design and Applications","raw_type":"proceedings-article"},{"id":"pmh:oai:arpi.unipi.it:11568/194000","is_oa":false,"landing_page_url":"http://hdl.handle.net/11568/194000","pdf_url":null,"source":{"id":"https://openalex.org/S4377196265","display_name":"CINECA IRIS Institutial research information system (University of Pisa)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I108290504","host_organization_name":"University of Pisa","host_organization_lineage":["https://openalex.org/I108290504"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.5199999809265137,"display_name":"Peace, Justice and strong institutions"},{"id":"https://metadata.un.org/sdg/10","score":0.4300000071525574,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1962499157","https://openalex.org/W1963605961","https://openalex.org/W1997336782","https://openalex.org/W2006255103","https://openalex.org/W2029073757","https://openalex.org/W2047136934","https://openalex.org/W2049623660","https://openalex.org/W2097308346","https://openalex.org/W2097747115","https://openalex.org/W2100718094","https://openalex.org/W2114830639","https://openalex.org/W2132914434","https://openalex.org/W2136063736","https://openalex.org/W2165874743","https://openalex.org/W2172030917","https://openalex.org/W2199502820","https://openalex.org/W3121318964","https://openalex.org/W3123908627","https://openalex.org/W4233622498","https://openalex.org/W4244359974","https://openalex.org/W4301689241","https://openalex.org/W6684578312","https://openalex.org/W6685474559"],"related_works":["https://openalex.org/W1676609285","https://openalex.org/W3148934225","https://openalex.org/W2351648145","https://openalex.org/W199725827","https://openalex.org/W4248896451","https://openalex.org/W1983856919","https://openalex.org/W2374029814","https://openalex.org/W2992253016","https://openalex.org/W2076529521","https://openalex.org/W2321313521"],"abstract_inverted_index":{"In":[0],"this":[1],"work":[2],"a":[3],"modified":[4,100],"spectral":[5,101],"clustering":[6,71,102],"algorithm":[7],"for":[8,22,29,39,94,122],"time-series":[9,59],"data":[10,24],"is":[11,16,88],"introduced.":[12],"The":[13,32,83],"presented":[14],"modification":[15],"to":[17,118],"replace":[18],"the":[19,45,56,70,96,112],"distance":[20,37],"measure":[21],"static":[23],"with":[25,99],"an":[26,91],"appropriate":[27],"one":[28],"time":[30,40,74],"series.":[31],"performed":[33],"analysis":[34],"considers":[35],"several":[36],"measures":[38],"series,":[41],"and":[42,51,66,103,124],"it":[43],"includes":[44],"use":[46,113],"of":[47,58,72,114],"different":[48,62],"similarity":[49],"graphs":[50],"graph":[52],"Laplacians.":[53],"We":[54],"consider":[55],"discrimination":[57],"generated":[60,76],"using":[61,77],"linear":[63,123],"ARMA":[64],"models,":[65],"we":[67],"also":[68],"investigated":[69],"nonlinear":[73,125],"series":[75],"autoregressive":[78],"conditional":[79],"heteroskedasticity":[80],"(ARCH)":[81],"models.":[82],"Hubert-Arabie":[84],"adjusted":[85],"Rand's":[86],"index":[87],"used":[89],"as":[90],"external":[92],"criterion":[93],"evaluating":[95],"partitions":[97],"obtained":[98],"various":[104],"linkage":[105],"algorithms.":[106],"Guidelines":[107],"are":[108],"discussed,":[109],"in":[110],"particular":[111],"cepstral":[115],"coefficients":[116],"proves":[117],"be":[119],"efficient":[120],"both":[121],"data.":[126]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}