{"id":"https://openalex.org/W2141428803","doi":"https://doi.org/10.1109/cbms.2009.5255269","title":"Finding structurally different medical data","display_name":"Finding structurally different medical data","publication_year":2009,"publication_date":"2009-08-01","ids":{"openalex":"https://openalex.org/W2141428803","doi":"https://doi.org/10.1109/cbms.2009.5255269","mag":"2141428803"},"language":"en","primary_location":{"id":"doi:10.1109/cbms.2009.5255269","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cbms.2009.5255269","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 22nd IEEE International Symposium on Computer-Based Medical Systems","raw_type":"proceedings-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/A5101558875","display_name":"Jessica Lin","orcid":"https://orcid.org/0000-0002-4887-0692"},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"education","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Jessica Lin","raw_affiliation_strings":["Computer Science Department, George Mason University, USA","Computer Science Dept., George Mason University"],"affiliations":[{"raw_affiliation_string":"Computer Science Department, George Mason University, USA","institution_ids":["https://openalex.org/I162714631"]},{"raw_affiliation_string":"Computer Science Dept., George Mason University","institution_ids":["https://openalex.org/I162714631"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101512990","display_name":"Yuan Li","orcid":"https://orcid.org/0009-0006-9301-9369"},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"education","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yuan Li","raw_affiliation_strings":["Computer Science Department, George Mason University, USA","Computer Science Dept., George Mason University"],"affiliations":[{"raw_affiliation_string":"Computer Science Department, George Mason University, USA","institution_ids":["https://openalex.org/I162714631"]},{"raw_affiliation_string":"Computer Science Dept., George Mason University","institution_ids":["https://openalex.org/I162714631"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5101558875"],"corresponding_institution_ids":["https://openalex.org/I162714631"],"apc_list":null,"apc_paid":null,"fwci":0.3571,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.60026525,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"1","issue":null,"first_page":"1","last_page":"8"},"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/T11309","display_name":"Music and Audio Processing","score":0.9606000185012817,"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/T11106","display_name":"Data Management and Algorithms","score":0.9549000263214111,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.7328251600265503},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7182982563972473},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.7092047929763794},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6851238012313843},{"id":"https://openalex.org/keywords/histogram","display_name":"Histogram","score":0.611339807510376},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5826584696769714},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5532519221305847},{"id":"https://openalex.org/keywords/nearest-neighbor-search","display_name":"Nearest neighbor search","score":0.5246586799621582},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4603734612464905},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.41931259632110596},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.35098403692245483},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3139772117137909},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.1048319935798645}],"concepts":[{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.7328251600265503},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7182982563972473},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.7092047929763794},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6851238012313843},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.611339807510376},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5826584696769714},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5532519221305847},{"id":"https://openalex.org/C116738811","wikidata":"https://www.wikidata.org/wiki/Q608751","display_name":"Nearest neighbor search","level":2,"score":0.5246586799621582},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4603734612464905},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.41931259632110596},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35098403692245483},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3139772117137909},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.1048319935798645},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","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/cbms.2009.5255269","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cbms.2009.5255269","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 22nd IEEE International Symposium on Computer-Based Medical Systems","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.386.9143","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.386.9143","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.gmu.edu/~jessica/publications/structural_cbms09.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W58346954","https://openalex.org/W109020964","https://openalex.org/W131856359","https://openalex.org/W140867740","https://openalex.org/W147860157","https://openalex.org/W1499049447","https://openalex.org/W1581502805","https://openalex.org/W1638203394","https://openalex.org/W1785021972","https://openalex.org/W1963737619","https://openalex.org/W1989280111","https://openalex.org/W1998871699","https://openalex.org/W2006783944","https://openalex.org/W2042591571","https://openalex.org/W2076249942","https://openalex.org/W2099562280","https://openalex.org/W2118506357","https://openalex.org/W2127218421","https://openalex.org/W2128061541","https://openalex.org/W2133184712","https://openalex.org/W2163336863","https://openalex.org/W2164274563","https://openalex.org/W2165612380","https://openalex.org/W2166064672","https://openalex.org/W3147178137","https://openalex.org/W4230960895","https://openalex.org/W6605731719","https://openalex.org/W6606145560","https://openalex.org/W6634966284","https://openalex.org/W6661122342","https://openalex.org/W6675017277","https://openalex.org/W6677936457"],"related_works":["https://openalex.org/W2107628111","https://openalex.org/W2394004323","https://openalex.org/W2398764543","https://openalex.org/W4298130764","https://openalex.org/W2804364458","https://openalex.org/W2027335291","https://openalex.org/W4210328553","https://openalex.org/W1980417906","https://openalex.org/W2132641928","https://openalex.org/W4310225030"],"abstract_inverted_index":{"For":[0,73],"more":[1,78],"than":[2],"one":[3],"decade,":[4],"time":[5,25,39,58,95,113,150],"series":[6,26,40,59,114,151],"similarity":[7,41,83],"search":[8,42],"has":[9],"been":[10,32],"given":[11],"a":[12,22,109],"great":[13],"deal":[14],"of":[15,50,120],"attention":[16],"by":[17,127],"data":[18],"mining":[19,130],"researchers.":[20],"As":[21],"result,":[23],"many":[24],"representations":[27],"and":[28,131,146],"distance":[29],"measures":[30],"have":[31],"proposed.":[33],"However,":[34],"most":[35],"existing":[36,52,142],"work":[37,54],"on":[38,44,85,148],"focuses":[43],"finding":[45],"shape-based":[46],"similarity.":[47],"While":[48],"some":[49],"the":[51,69,82,86,118,128,141],"approaches":[53],"well":[55],"for":[56,93,112],"short":[57],"data,":[60,115],"they":[61,98],"typically":[62],"fail":[63],"to":[64,80,117],"produce":[65],"satisfactory":[66],"results":[67],"when":[68],"sequence":[70],"is":[71,77,90,124],"long.":[72],"long":[74],"sequences,":[75],"it":[76],"appropriate":[79],"consider":[81],"based":[84],"higher-level":[87],"structures.":[88],"This":[89],"particularly":[91],"true":[92],"medical":[94,149],"series,":[96],"as":[97],"often":[99],"are":[100],"not":[101],"perfectly":[102],"aligned.":[103],"In":[104],"this":[105],"work,":[106],"we":[107],"present":[108],"histogram-based":[110],"representation":[111],"similar":[116],"\"bag":[119],"words\"":[121],"approach":[122,139],"that":[123,137],"widely":[125],"accepted":[126],"text":[129],"information":[132],"retrieval":[133],"communities.":[134],"We":[135],"show":[136],"our":[138],"outperforms":[140],"methods":[143],"in":[144],"clustering":[145],"classification":[147],"obtained":[152],"from":[153],"PhysioBank.":[154]},"counts_by_year":[{"year":2020,"cited_by_count":2},{"year":2014,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}