{"id":"https://openalex.org/W3105712096","doi":"https://doi.org/10.1109/isgt-europe47291.2020.9248796","title":"Application of the k-medoids Partitioning Algorithm for Clustering of Time Series Data","display_name":"Application of the k-medoids Partitioning Algorithm for Clustering of Time Series Data","publication_year":2020,"publication_date":"2020-10-26","ids":{"openalex":"https://openalex.org/W3105712096","doi":"https://doi.org/10.1109/isgt-europe47291.2020.9248796","mag":"3105712096"},"language":"en","primary_location":{"id":"doi:10.1109/isgt-europe47291.2020.9248796","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isgt-europe47291.2020.9248796","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://pure.manchester.ac.uk/ws/files/183517225/Application_of_the_k_medoids_partitioning_algorithm_for_clustering_of_time_series_data_Pure.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5087994198","display_name":"Ana Radovanovi\u0107","orcid":"https://orcid.org/0000-0001-8676-0016"},"institutions":[{"id":"https://openalex.org/I28407311","display_name":"University of Manchester","ror":"https://ror.org/027m9bs27","country_code":"GB","type":"education","lineage":["https://openalex.org/I28407311"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Ana Radovanovic","raw_affiliation_strings":["The University of Manchester, Manchester, UK"],"affiliations":[{"raw_affiliation_string":"The University of Manchester, Manchester, UK","institution_ids":["https://openalex.org/I28407311"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007856354","display_name":"Xinlin Ye","orcid":"https://orcid.org/0000-0002-6352-558X"},"institutions":[{"id":"https://openalex.org/I28407311","display_name":"University of Manchester","ror":"https://ror.org/027m9bs27","country_code":"GB","type":"education","lineage":["https://openalex.org/I28407311"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Xinlin Ye","raw_affiliation_strings":["The University of Manchester, Manchester, UK"],"affiliations":[{"raw_affiliation_string":"The University of Manchester, Manchester, UK","institution_ids":["https://openalex.org/I28407311"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084361222","display_name":"Jovica V. Milanovi\u0107","orcid":"https://orcid.org/0000-0002-0931-137X"},"institutions":[{"id":"https://openalex.org/I28407311","display_name":"University of Manchester","ror":"https://ror.org/027m9bs27","country_code":"GB","type":"education","lineage":["https://openalex.org/I28407311"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Jovica V. Milanovic","raw_affiliation_strings":["The University of Manchester, Manchester, UK"],"affiliations":[{"raw_affiliation_string":"The University of Manchester, Manchester, UK","institution_ids":["https://openalex.org/I28407311"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027107360","display_name":"Nina Milosavljevic","orcid":"https://orcid.org/0000-0001-8859-1797"},"institutions":[{"id":"https://openalex.org/I28407311","display_name":"University of Manchester","ror":"https://ror.org/027m9bs27","country_code":"GB","type":"education","lineage":["https://openalex.org/I28407311"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Nina Milosavljevic","raw_affiliation_strings":["Division of Neuroscience and Experimental Psychology, The University of Manchester, Manchester, UK"],"affiliations":[{"raw_affiliation_string":"Division of Neuroscience and Experimental Psychology, The University of Manchester, Manchester, UK","institution_ids":["https://openalex.org/I28407311"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009436389","display_name":"Riccardo Storchi","orcid":"https://orcid.org/0000-0002-3656-7514"},"institutions":[{"id":"https://openalex.org/I28407311","display_name":"University of Manchester","ror":"https://ror.org/027m9bs27","country_code":"GB","type":"education","lineage":["https://openalex.org/I28407311"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Riccardo Storchi","raw_affiliation_strings":["Division of Neuroscience and Experimental Psychology, The University of Manchester, Manchester, UK"],"affiliations":[{"raw_affiliation_string":"Division of Neuroscience and Experimental Psychology, The University of Manchester, Manchester, UK","institution_ids":["https://openalex.org/I28407311"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5087994198"],"corresponding_institution_ids":["https://openalex.org/I28407311"],"apc_list":null,"apc_paid":null,"fwci":0.3044,"has_fulltext":true,"cited_by_count":8,"citation_normalized_percentile":{"value":0.56028923,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"645","last_page":"649"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9997000098228455,"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":0.9997000098228455,"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":0.9850000143051147,"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/T10320","display_name":"Neural Networks and Applications","score":0.9663000106811523,"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/cluster-analysis","display_name":"Cluster analysis","score":0.8773895502090454},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7260792255401611},{"id":"https://openalex.org/keywords/cure-data-clustering-algorithm","display_name":"CURE data clustering algorithm","score":0.7148889899253845},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.6787174344062805},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.6056430339813232},{"id":"https://openalex.org/keywords/data-stream-clustering","display_name":"Data stream clustering","score":0.5766066908836365},{"id":"https://openalex.org/keywords/canopy-clustering-algorithm","display_name":"Canopy clustering algorithm","score":0.5741490125656128},{"id":"https://openalex.org/keywords/medoid","display_name":"Medoid","score":0.566234290599823},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5326549410820007},{"id":"https://openalex.org/keywords/k-medoids","display_name":"k-medoids","score":0.4872835874557495},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.48407259583473206},{"id":"https://openalex.org/keywords/fuzzy-clustering","display_name":"Fuzzy clustering","score":0.4522625803947449},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.43615877628326416},{"id":"https://openalex.org/keywords/clustering-high-dimensional-data","display_name":"Clustering high-dimensional data","score":0.43607693910598755},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2650313973426819},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2310822308063507}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.8773895502090454},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7260792255401611},{"id":"https://openalex.org/C33704608","wikidata":"https://www.wikidata.org/wiki/Q5014717","display_name":"CURE data clustering algorithm","level":4,"score":0.7148889899253845},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6787174344062805},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.6056430339813232},{"id":"https://openalex.org/C193143536","wikidata":"https://www.wikidata.org/wiki/Q5227360","display_name":"Data stream clustering","level":5,"score":0.5766066908836365},{"id":"https://openalex.org/C104047586","wikidata":"https://www.wikidata.org/wiki/Q5033439","display_name":"Canopy clustering algorithm","level":4,"score":0.5741490125656128},{"id":"https://openalex.org/C63085389","wikidata":"https://www.wikidata.org/wiki/Q4287912","display_name":"Medoid","level":3,"score":0.566234290599823},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5326549410820007},{"id":"https://openalex.org/C65932786","wikidata":"https://www.wikidata.org/wiki/Q3191282","display_name":"k-medoids","level":5,"score":0.4872835874557495},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.48407259583473206},{"id":"https://openalex.org/C17212007","wikidata":"https://www.wikidata.org/wiki/Q5511111","display_name":"Fuzzy clustering","level":3,"score":0.4522625803947449},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.43615877628326416},{"id":"https://openalex.org/C184509293","wikidata":"https://www.wikidata.org/wiki/Q5136711","display_name":"Clustering high-dimensional data","level":3,"score":0.43607693910598755},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2650313973426819},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2310822308063507},{"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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/isgt-europe47291.2020.9248796","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isgt-europe47291.2020.9248796","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe)","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.atira.dk:publications/ad142637-97d8-4bf5-b308-a5c2e9f57a16","is_oa":true,"landing_page_url":"https://research.manchester.ac.uk/en/publications/ad142637-97d8-4bf5-b308-a5c2e9f57a16","pdf_url":"https://pure.manchester.ac.uk/ws/files/183517225/Application_of_the_k_medoids_partitioning_algorithm_for_clustering_of_time_series_data_Pure.pdf","source":{"id":"https://openalex.org/S4306400662","display_name":"Research Explorer (The University of Manchester)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I28407311","host_organization_name":"University of Manchester","host_organization_lineage":["https://openalex.org/I28407311"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Radovanovic, A, Ye, X, Milanovic, J V, Milosavljevic, N & Storchi, R 2020, 'Application of the k-medoids Partitioning Algorithm for Clustering of Time Series Data', Paper presented at 2020 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), The Hague, Netherlands, 26/10/20 - 28/10/20 pp. 645-649. https://doi.org/10.1109/ISGT-Europe47291.2020.9248796","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":{"id":"pmh:oai:pure.atira.dk:publications/ad142637-97d8-4bf5-b308-a5c2e9f57a16","is_oa":true,"landing_page_url":"https://research.manchester.ac.uk/en/publications/ad142637-97d8-4bf5-b308-a5c2e9f57a16","pdf_url":"https://pure.manchester.ac.uk/ws/files/183517225/Application_of_the_k_medoids_partitioning_algorithm_for_clustering_of_time_series_data_Pure.pdf","source":{"id":"https://openalex.org/S4306400662","display_name":"Research Explorer (The University of Manchester)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I28407311","host_organization_name":"University of Manchester","host_organization_lineage":["https://openalex.org/I28407311"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Radovanovic, A, Ye, X, Milanovic, J V, Milosavljevic, N & Storchi, R 2020, 'Application of the k-medoids Partitioning Algorithm for Clustering of Time Series Data', Paper presented at 2020 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), The Hague, Netherlands, 26/10/20 - 28/10/20 pp. 645-649. https://doi.org/10.1109/ISGT-Europe47291.2020.9248796","raw_type":"info:eu-repo/semantics/publishedVersion"},"sustainable_development_goals":[{"score":0.8700000047683716,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[{"id":"https://openalex.org/G302216903","display_name":null,"funder_award_id":"77343","funder_id":"https://openalex.org/F4320332999","funder_display_name":"Horizon 2020 Framework Programme"},{"id":"https://openalex.org/G6939042904","display_name":null,"funder_award_id":"773430","funder_id":"https://openalex.org/F4320332999","funder_display_name":"Horizon 2020 Framework Programme"}],"funders":[{"id":"https://openalex.org/F4320332999","display_name":"Horizon 2020 Framework Programme","ror":"https://ror.org/00k4n6c32"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3105712096.pdf","grobid_xml":"https://content.openalex.org/works/W3105712096.grobid-xml"},"referenced_works_count":15,"referenced_works":["https://openalex.org/W262555347","https://openalex.org/W1894414046","https://openalex.org/W1992419399","https://openalex.org/W2007749588","https://openalex.org/W2008348094","https://openalex.org/W2091921805","https://openalex.org/W2097747115","https://openalex.org/W2110366306","https://openalex.org/W2140190241","https://openalex.org/W2169889617","https://openalex.org/W2184638288","https://openalex.org/W2257803164","https://openalex.org/W2479531384","https://openalex.org/W2481053337","https://openalex.org/W2902491562"],"related_works":["https://openalex.org/W2088480663","https://openalex.org/W2386865990","https://openalex.org/W4301002638","https://openalex.org/W2967990367","https://openalex.org/W2371010743","https://openalex.org/W3088133960","https://openalex.org/W4253632195","https://openalex.org/W2163563073","https://openalex.org/W2079580042","https://openalex.org/W3115293824"],"abstract_inverted_index":{"Data":[0],"clustering":[1,30,51,65,71,102],"has":[2,44],"been":[3],"widely":[4],"applied":[5],"in":[6,9,101,126],"numerous":[7],"areas":[8],"order":[10],"to":[11],"pave":[12],"the":[13,24,28,46,60,67,108,111,116,119],"way":[14],"for":[15,48,70],"adequate":[16],"and":[17,21,94,115],"efficient":[18],"modelling,":[19],"control":[20],"operation.":[22],"In":[23],"past,":[25],"most":[26],"of":[27,40,59,62,72,87,110,118,122],"data":[29,43,106],"was":[31],"carried":[32],"out":[33],"on":[34,83],"static":[35],"data.":[36,97],"However,":[37],"wider":[38],"application":[39],"time":[41,49,75],"series":[42,50,76],"increased":[45],"need":[47],"techniques.":[52],"This":[53],"paper":[54],"presents":[55],"a":[56,63,88],"comprehensive":[57],"analysis":[58],"applicability":[61],"standard":[64],"algorithm,":[66,69],"k-medoids":[68,79],"two":[73],"diverse":[74],"datasets.":[77],"The":[78,98],"algorithm":[80],"is":[81],"tested":[82],"dynamic":[84],"power":[85],"responses":[86],"hybrid":[89],"renewable":[90],"energy":[91],"source":[92],"plant":[93],"neuroscience":[95],"spike-train":[96],"main":[99],"stages":[100],"process,":[103],"that":[104],"is,":[105],"processing,":[107],"selection":[109],"optimal":[112,120],"distance":[113],"measure":[114],"estimation":[117],"number":[121],"clusters,":[123],"are":[124],"analyzed":[125],"detail.":[127]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-10-10T00:00:00"}
