{"id":"https://openalex.org/W3086856410","doi":"https://doi.org/10.1109/bigdata50022.2020.9378478","title":"A new heuristic algorithm for fast k-segmentation","display_name":"A new heuristic algorithm for fast k-segmentation","publication_year":2020,"publication_date":"2020-12-10","ids":{"openalex":"https://openalex.org/W3086856410","doi":"https://doi.org/10.1109/bigdata50022.2020.9378478","mag":"3086856410"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata50022.2020.9378478","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata50022.2020.9378478","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2009.05148","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5089841175","display_name":"Sabarish Vadarevu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sabarish Vadarevu","raw_affiliation_strings":["Akridata India Pvt. Ltd., Bengaluru, India","Akridata India Pvt. Ltd.,Bengaluru,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Akridata India Pvt. Ltd., Bengaluru, India","institution_ids":[]},{"raw_affiliation_string":"Akridata India Pvt. Ltd.,Bengaluru,India","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073023433","display_name":"Vijay Karamcheti","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vijay Karamcheti","raw_affiliation_strings":["Akridata Inc., Los Altos, USA","Akridata Inc.,Los Altos,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Akridata Inc., Los Altos, USA","institution_ids":[]},{"raw_affiliation_string":"Akridata Inc.,Los Altos,USA","institution_ids":[]}]}],"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":0.11079422,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"156","issue":null,"first_page":"651","last_page":"658"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.998199999332428,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.998199999332428,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9976000189781189,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9948999881744385,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.7151796817779541},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.681946873664856},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6390904784202576},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.5118164420127869},{"id":"https://openalex.org/keywords/heuristic","display_name":"Heuristic","score":0.4610845446586609},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.45763424038887024},{"id":"https://openalex.org/keywords/time-complexity","display_name":"Time complexity","score":0.4229070246219635},{"id":"https://openalex.org/keywords/piecewise-linear-function","display_name":"Piecewise linear function","score":0.4201023280620575},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3267000913619995},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.22632291913032532}],"concepts":[{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.7151796817779541},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.681946873664856},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6390904784202576},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.5118164420127869},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.4610845446586609},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.45763424038887024},{"id":"https://openalex.org/C311688","wikidata":"https://www.wikidata.org/wiki/Q2393193","display_name":"Time complexity","level":2,"score":0.4229070246219635},{"id":"https://openalex.org/C17095337","wikidata":"https://www.wikidata.org/wiki/Q2375229","display_name":"Piecewise linear function","level":2,"score":0.4201023280620575},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3267000913619995},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22632291913032532},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/bigdata50022.2020.9378478","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata50022.2020.9378478","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2009.05148","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2009.05148","pdf_url":"https://arxiv.org/pdf/2009.05148","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"mag:3086856410","is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/2009.05148","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.2009.05148","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2009.05148","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2009.05148","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2009.05148","pdf_url":"https://arxiv.org/pdf/2009.05148","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W639907764","https://openalex.org/W1578551615","https://openalex.org/W1975684011","https://openalex.org/W2036414171","https://openalex.org/W2042190395","https://openalex.org/W2047287351","https://openalex.org/W2064697979","https://openalex.org/W2125954346","https://openalex.org/W2270810228","https://openalex.org/W2515822248","https://openalex.org/W2909693411","https://openalex.org/W2963163009","https://openalex.org/W3010896178","https://openalex.org/W3035564946","https://openalex.org/W3098561233","https://openalex.org/W4238553616","https://openalex.org/W6634608682","https://openalex.org/W6678824329","https://openalex.org/W6920837612"],"related_works":["https://openalex.org/W2017519713","https://openalex.org/W3000256619","https://openalex.org/W2889974534","https://openalex.org/W2081818066","https://openalex.org/W3209412595","https://openalex.org/W2088649052","https://openalex.org/W2154321958","https://openalex.org/W3012337127","https://openalex.org/W2753234324","https://openalex.org/W1556611014","https://openalex.org/W2584490639","https://openalex.org/W2208501551","https://openalex.org/W2090286885","https://openalex.org/W2045037944","https://openalex.org/W2133123111","https://openalex.org/W2216735140","https://openalex.org/W2342366399","https://openalex.org/W3011951626","https://openalex.org/W2902824893","https://openalex.org/W3112024879"],"abstract_inverted_index":{"The":[0,153],"k-segmentation":[1,63],"of":[2,69,100,138,156,166,185,195,213,238],"a":[3,22,73,98,131,192,200,249],"time-indexed":[4],"data":[5,244,252],"stream":[6],"is":[7,85,106,120,148,221],"used":[8,29,142],"to":[9,30,37,48,163,174,223,248],"partition":[10],"it":[11],"into":[12],"k":[13],"piecewise-linear":[14],"segments,":[15,41,46],"so":[16],"that":[17,207],"each":[18],"linear":[19],"segment":[20],"has":[21],"meaningful":[23],"interpretation.":[24],"Such":[25],"segmentation":[26,60,219],"may":[27],"be":[28],"summarize":[31],"large":[32,193],"datasets":[33],"with":[34,94,180,209,246],"small":[35],"sub-samples,":[36],"identify":[38],"anomalies":[39],"within":[40,254],"detect":[42],"change":[43],"points":[44,253],"between":[45,76],"and":[47,58,79,113,119,204,228],"select":[49],"critical":[50],"subsets":[51],"for":[52,62,111,116,125],"training":[53],"machine":[54],"learning":[55],"models.":[56],"Exact":[57],"approximate":[59],"methods":[61,96],"exist":[64],"in":[65,87,150],"the":[66,101,122,140,160,164,169,186,214,225,229],"literature.":[67],"Each":[68],"these":[70],"algorithms":[71],"provides":[72,91],"different":[74,175,181],"trade-off":[75],"computational":[77,102,231],"complexity":[78,232],"accuracy.":[80],"A":[81],"novel":[82],"heuristic":[83],"algorithm":[84,105,110,115,124,128,161,188],"proposed":[86,187],"this":[88,151],"paper":[89],"which":[90],"accuracies":[92],"competitive":[93],"exact":[95],"at":[97],"fraction":[99],"expense.The":[103],"new":[104],"inspired":[107],"by":[108],"Lloyd's":[109],"K-Means":[112],"Lloyd-Max":[114],"scalar":[117],"quantization,":[118],"called":[121],"LM":[123,202,239],"convenience.":[126],"This":[127,236],"iteratively":[129],"minimizes":[130],"cost":[132,147,157],"function":[133],"from":[134],"any":[135],"given":[136],"initialisation":[137],"partitions;":[139],"commonly":[141],"L":[143],"<sub":[144],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[145],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">2</sub>":[146],"chosen":[149],"paper.":[152],"greedy":[154],"nature":[155],"minimization":[158],"makes":[159],"sensitive":[162],"choice":[165],"initialisation.":[167],"However,":[168],"same":[170],"sensitivity":[171],"allows":[172],"convergence":[173],"local":[176],"optima":[177],"when":[178],"starting":[179],"initialisations.":[182],"Three":[183],"variants":[184],"are":[189],"tested":[190],"over":[191,243],"number":[194],"synthetic":[196],"datasets,":[197],"one":[198],"being":[199],"standalone":[201],"implementation,":[203],"two":[205,216],"others":[206],"combine":[208],"existing":[210],"algorithms.":[211,235],"One":[212],"latter":[215],"-":[217,220],"LM-enhanced-Bottom-Up":[218],"found":[222],"have":[224],"best":[226],"accuracy":[227],"lowest":[230],"among":[233],"all":[234],"variant":[237],"can":[240],"provide":[241],"k-segmentations":[242],"sets":[245],"up":[247],"million":[250],"high-dimensional":[251],"several":[255],"seconds.":[256]},"counts_by_year":[],"updated_date":"2026-07-01T08:55:40.977307","created_date":"2025-10-10T00:00:00"}
