{"id":"https://openalex.org/W2071971241","doi":"https://doi.org/10.1145/1014052.1014103","title":"Effective localized regression for damage detection in large complex mechanical structures","display_name":"Effective localized regression for damage detection in large complex mechanical structures","publication_year":2004,"publication_date":"2004-08-22","ids":{"openalex":"https://openalex.org/W2071971241","doi":"https://doi.org/10.1145/1014052.1014103","mag":"2071971241"},"language":"en","primary_location":{"id":"doi:10.1145/1014052.1014103","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1014052.1014103","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining","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/A5046071383","display_name":"Aleksandar Lazarevi\u0107","orcid":null},"institutions":[{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Aleksandar Lazarevic","raw_affiliation_strings":["University of Minnesota, United Technologies, Minneapolis, MN","University of Minnesota, United Technologies, Minneapolis, MN#TAB#"],"affiliations":[{"raw_affiliation_string":"University of Minnesota, United Technologies, Minneapolis, MN","institution_ids":["https://openalex.org/I130238516"]},{"raw_affiliation_string":"University of Minnesota, United Technologies, Minneapolis, MN#TAB#","institution_ids":["https://openalex.org/I130238516"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016293195","display_name":"Ramdev Kanapady","orcid":null},"institutions":[{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ramdev Kanapady","raw_affiliation_strings":["University of Minnesota, Minneapolis, MN","University of Minnesota , Minneapolis, Mn"],"affiliations":[{"raw_affiliation_string":"University of Minnesota, Minneapolis, MN","institution_ids":["https://openalex.org/I130238516"]},{"raw_affiliation_string":"University of Minnesota , Minneapolis, Mn","institution_ids":["https://openalex.org/I130238516"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014354500","display_name":"Chandrika Kamath","orcid":"https://orcid.org/0000-0002-0188-8174"},"institutions":[{"id":"https://openalex.org/I1282311441","display_name":"Lawrence Livermore National Laboratory","ror":"https://ror.org/041nk4h53","country_code":"US","type":"facility","lineage":["https://openalex.org/I1282311441","https://openalex.org/I1330989302","https://openalex.org/I198811213","https://openalex.org/I4210138311"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chandrika Kamath","raw_affiliation_strings":["Lawrence Livermore National Lab., Livermore, CA","[Lawrence Livermore National Labs, Livermore, CA]"],"affiliations":[{"raw_affiliation_string":"Lawrence Livermore National Lab., Livermore, CA","institution_ids":["https://openalex.org/I1282311441"]},{"raw_affiliation_string":"[Lawrence Livermore National Labs, Livermore, CA]","institution_ids":["https://openalex.org/I1282311441"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5046071383"],"corresponding_institution_ids":["https://openalex.org/I130238516"],"apc_list":null,"apc_paid":null,"fwci":1.1749,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.81730769,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"450","last_page":"459"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10534","display_name":"Structural Health Monitoring Techniques","score":0.9972000122070312,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"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/T10534","display_name":"Structural Health Monitoring Techniques","score":0.9972000122070312,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"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/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9861000180244446,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"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/T13832","display_name":"Advanced Decision-Making Techniques","score":0.9523000121116638,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.7913700342178345},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.6320262551307678},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6260938048362732},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5107529163360596},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.5088884830474854},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5013031959533691},{"id":"https://openalex.org/keywords/cluster","display_name":"Cluster (spacecraft)","score":0.4932531714439392},{"id":"https://openalex.org/keywords/data-structure","display_name":"Data structure","score":0.4448935389518738},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.4329034686088562},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.41981241106987},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3479312062263489},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.334486722946167},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3231458067893982},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.25816982984542847},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16915416717529297},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.13517653942108154},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.09541147947311401},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.07440781593322754}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7913700342178345},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6320262551307678},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6260938048362732},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5107529163360596},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.5088884830474854},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5013031959533691},{"id":"https://openalex.org/C164866538","wikidata":"https://www.wikidata.org/wiki/Q367351","display_name":"Cluster (spacecraft)","level":2,"score":0.4932531714439392},{"id":"https://openalex.org/C162319229","wikidata":"https://www.wikidata.org/wiki/Q175263","display_name":"Data structure","level":2,"score":0.4448935389518738},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.4329034686088562},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.41981241106987},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3479312062263489},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.334486722946167},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3231458067893982},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.25816982984542847},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16915416717529297},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.13517653942108154},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.09541147947311401},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.07440781593322754},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/1014052.1014103","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1014052.1014103","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306084","display_name":"U.S. Department of Energy","ror":"https://ror.org/01bj3aw27"},{"id":"https://openalex.org/F4320338286","display_name":"Lawrence Livermore National Laboratory","ror":"https://ror.org/041nk4h53"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W207784250","https://openalex.org/W596980666","https://openalex.org/W1566538838","https://openalex.org/W1570713908","https://openalex.org/W1578212128","https://openalex.org/W1621791442","https://openalex.org/W1966332297","https://openalex.org/W1974538445","https://openalex.org/W2000308476","https://openalex.org/W2008926326","https://openalex.org/W2038381734","https://openalex.org/W2074535414","https://openalex.org/W2085637725","https://openalex.org/W2126751256","https://openalex.org/W2143908786","https://openalex.org/W2155482699","https://openalex.org/W2169180968","https://openalex.org/W2230977749","https://openalex.org/W2913340405","https://openalex.org/W4291439952","https://openalex.org/W4300874750"],"related_works":["https://openalex.org/W31220157","https://openalex.org/W2312753042","https://openalex.org/W4289356671","https://openalex.org/W2389155397","https://openalex.org/W2165884543","https://openalex.org/W3186837933","https://openalex.org/W2368989808","https://openalex.org/W2034959125","https://openalex.org/W2355687852","https://openalex.org/W2621086889"],"abstract_inverted_index":{"In":[0,92],"this":[1],"paper,":[2],"we":[3,102,151,174,180],"propose":[4,103],"a":[5,104,116,120,154,182,203,233],"novel":[6],"data":[7,70,90,121,140,149,157,161,167,213],"mining":[8,71],"technique":[9],"for":[10,165,186,195],"the":[11,16,27,50,57,96,143,218,227,241],"efficient":[12,254],"damage":[13,131,230],"detection":[14],"within":[15],"large-scale":[17],"complex":[18,38],"mechanical":[19,22],"structures.":[20],"Every":[21],"structure":[23,35,46,78,135,177,190,197,222],"is":[24,65,247],"defined":[25],"by":[26],"set":[28,59,205],"of":[29,45,60,99,111,123,130,156,188,206,220,229],"finite":[30],"elements":[31,136,178,198],"that":[32,85,109,137,169,215,221,240],"are":[33,86,199],"called":[34],"elements.":[36,191],"Large-scale":[37],"structures":[39],"may":[40],"have":[41],"extremely":[42],"large":[43,234],"number":[44],"elements,":[47],"and":[48,125,179,210,251],"predicting":[49,127],"failure":[51,75,219],"in":[52,76,133,232],"every":[53],"single":[54],"element":[55,79],"using":[56,81,201],"original":[58],"natural":[61,208],"frequencies":[62,209],"as":[63,261,263],"features":[64],"exceptionally":[66],"time-consuming":[67],"task.":[68],"Traditional":[69],"techniques":[72],"simply":[73],"predict":[74],"each":[77,147,166,187],"individually":[80],"global":[82,264],"prediction":[83,231,265],"models":[84,194],"built":[87,144],"considering":[88],"all":[89],"records.":[91],"order":[93],"to":[94,139,171,217],"reduce":[95],"time":[97],"complexity":[98],"these":[100,189],"models,":[101],"localized":[105,183,243],"clustering-regression":[106,244],"based":[107,245],"approach":[108,246],"consists":[110],"two":[112],"phases:":[113],"(1)":[114],"building":[115],"local":[117],"cluster":[118,155],"around":[119,162],"record":[122,168],"interest":[124],"(2)":[126],"an":[128],"intensity":[129],"only":[132,202],"those":[134,212],"correspond":[138,216],"records":[141,158,214],"from":[142,159],"cluster.":[145],"For":[146],"test":[148],"record,":[150],"first":[152],"build":[153,181],"training":[160],"it.":[163],"Then,":[164],"belongs":[170],"discovered":[172],"cluster,":[173],"identify":[175],"corresponding":[176],"regression":[184,193],"model":[185],"These":[192],"specific":[196,204],"constructed":[200],"relevant":[207],"merely":[211],"element.":[223],"Experiments":[224],"performed":[225],"on":[226],"problem":[228],"electric":[235],"transmission":[236],"tower":[237],"frame":[238],"indicate":[239],"proposed":[242],"significantly":[248],"more":[249,252],"accurate":[250],"computationally":[253],"than":[255],"our":[256],"previous":[257],"hierarchical":[258],"clustering":[259],"approach,":[260],"well":[262],"models.":[266]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
