{"id":"https://openalex.org/W296435875","doi":null,"title":"Developing efficient algorithms for data mining large scale high dimensional data","display_name":"Developing efficient algorithms for data mining large scale high dimensional data","publication_year":2013,"publication_date":"2013-01-01","ids":{"openalex":"https://openalex.org/W296435875","mag":"296435875"},"language":"en","primary_location":{"id":"pmh:oai:escholarship.org/ark:/13030/qt660316zp","doi":null,"is_oa":true,"landing_page_url":"https://escholarship.org/uc/item/660316zp","pdf_url":"https://escholarship.org/content/qt660316zp/qt660316zp.pdf","source":{"id":"https://openalex.org/S4306400115","display_name":"eScholarship (California Digital Library)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I2801248553","host_organization_name":"California Digital Library","host_organization_lineage":["https://openalex.org/I2801248553"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"etd"},"volume":null,"type":"dissertation","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://escholarship.org/content/qt660316zp/qt660316zp.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5057110882","display_name":"Jesin Zakaria","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Jesin Zakaria","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5057110882"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9926999807357788,"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":0.9926999807357788,"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.006200000178068876,"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"}},{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.0005000000237487257,"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/time-series-clustering","display_name":"time series clustering","score":0.9070000052452087},{"id":"https://openalex.org/keywords/noisy-data","display_name":"noisy data","score":0.5040000081062317},{"id":"https://openalex.org/keywords/high-dimensional-data","display_name":"high-dimensional data","score":0.5609999895095825},{"id":"https://openalex.org/keywords/big-data-mining","display_name":"big data mining","score":0.7179999947547913},{"id":"https://openalex.org/keywords/anytime-algorithms","display_name":"anytime algorithms","score":0.6909999847412109},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"dimensionality reduction","score":0.8360000252723694},{"id":"https://openalex.org/keywords/electrocardiography","display_name":"electrocardiogram","score":0.7170000076293945},{"id":"https://openalex.org/keywords/stock-market-data","display_name":"stock market data","score":0.9459999799728394},{"id":"https://openalex.org/keywords/spatial-data","display_name":"geospatial data","score":0.9539999961853027},{"id":"https://openalex.org/keywords/audio-data","display_name":"audio data","score":0.8379999995231628},{"id":"https://openalex.org/keywords/image-data","display_name":"image data","score":0.9739999771118164}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7982920408248901},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7496761083602905},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5957357883453369},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.5444397330284119},{"id":"https://openalex.org/C193143536","wikidata":"https://www.wikidata.org/wiki/Q5227360","display_name":"Data stream clustering","level":5,"score":0.5262157917022705},{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.47943705320358276},{"id":"https://openalex.org/C88006597","wikidata":"https://www.wikidata.org/wiki/Q690117","display_name":"Disk formatting","level":2,"score":0.4448016881942749},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.4384951889514923},{"id":"https://openalex.org/C33704608","wikidata":"https://www.wikidata.org/wiki/Q5014717","display_name":"CURE data clustering algorithm","level":4,"score":0.39864489436149597},{"id":"https://openalex.org/C17212007","wikidata":"https://www.wikidata.org/wiki/Q5511111","display_name":"Fuzzy clustering","level":3,"score":0.3868613839149475},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.29877322912216187},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:escholarship.org/ark:/13030/qt660316zp","doi":null,"is_oa":true,"landing_page_url":"https://escholarship.org/uc/item/660316zp","pdf_url":"https://escholarship.org/content/qt660316zp/qt660316zp.pdf","source":{"id":"https://openalex.org/S4306400115","display_name":"eScholarship (California Digital Library)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I2801248553","host_organization_name":"California Digital Library","host_organization_lineage":["https://openalex.org/I2801248553"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"etd"},{"id":"mag:296435875","doi":null,"is_oa":false,"landing_page_url":"https://escholarship.org/content/qt660316zp/qt660316zp.pdf?t=n29zfa","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":{"id":"pmh:oai:escholarship.org/ark:/13030/qt660316zp","doi":null,"is_oa":true,"landing_page_url":"https://escholarship.org/uc/item/660316zp","pdf_url":"https://escholarship.org/content/qt660316zp/qt660316zp.pdf","source":{"id":"https://openalex.org/S4306400115","display_name":"eScholarship (California Digital Library)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I2801248553","host_organization_name":"California Digital Library","host_organization_lineage":["https://openalex.org/I2801248553"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"etd"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G2101647459","display_name":"Tools to Mine and Index Trajectories of Physical Artifacts","funder_award_id":"0803410","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8377684302","display_name":"III-CXT-Large: Collaborative Research: Interactive and intelligent searching of biological images by query and network navigation with learning capabilities","funder_award_id":"0808770","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W296435875.pdf","grobid_xml":"https://content.openalex.org/works/W296435875.grobid-xml"},"referenced_works_count":65,"referenced_works":["https://openalex.org/W22745672","https://openalex.org/W58346954","https://openalex.org/W116902681","https://openalex.org/W1721182246","https://openalex.org/W1750379408","https://openalex.org/W1952331239","https://openalex.org/W1965763723","https://openalex.org/W1970741763","https://openalex.org/W1971341839","https://openalex.org/W1989280111","https://openalex.org/W1989993068","https://openalex.org/W1990947928","https://openalex.org/W1996329869","https://openalex.org/W2002859524","https://openalex.org/W2004667770","https://openalex.org/W2006783944","https://openalex.org/W2011208599","https://openalex.org/W2013957985","https://openalex.org/W2029438113","https://openalex.org/W2031325729","https://openalex.org/W2033403400","https://openalex.org/W2034370406","https://openalex.org/W2034696724","https://openalex.org/W2036131961","https://openalex.org/W2040162990","https://openalex.org/W2058168013","https://openalex.org/W2074526138","https://openalex.org/W2078559879","https://openalex.org/W2083236658","https://openalex.org/W2083561187","https://openalex.org/W2087974907","https://openalex.org/W2089988906","https://openalex.org/W2090042627","https://openalex.org/W2097747115","https://openalex.org/W2098089331","https://openalex.org/W2098759488","https://openalex.org/W2100718094","https://openalex.org/W2104241941","https://openalex.org/W2109243512","https://openalex.org/W2110908910","https://openalex.org/W2113746910","https://openalex.org/W2115330604","https://openalex.org/W2122099933","https://openalex.org/W2122496402","https://openalex.org/W2129066856","https://openalex.org/W2130253467","https://openalex.org/W2140324756","https://openalex.org/W2141012957","https://openalex.org/W2141536962","https://openalex.org/W2146424733","https://openalex.org/W2148298736","https://openalex.org/W2149706766","https://openalex.org/W2150563191","https://openalex.org/W2152497905","https://openalex.org/W2153617971","https://openalex.org/W2163316275","https://openalex.org/W2163704882","https://openalex.org/W2165612380","https://openalex.org/W2169944034","https://openalex.org/W2170330565","https://openalex.org/W2200222898","https://openalex.org/W2396102635","https://openalex.org/W2403962807","https://openalex.org/W2405238707","https://openalex.org/W2582697902"],"related_works":["https://openalex.org/W6990767583","https://openalex.org/W2766654293","https://openalex.org/W2400411405","https://openalex.org/W2556809800","https://openalex.org/W1789163674","https://openalex.org/W7099846209","https://openalex.org/W187044068","https://openalex.org/W1935854638","https://openalex.org/W100511904","https://openalex.org/W7028537192"],"abstract_inverted_index":{"Data":[0],"mining":[1,71,126,329,367],"and":[2,37,42,190,245,262,325,348,351],"knowledge":[3,356],"discovery":[4,259,279,310,357],"has":[5],"attracted":[6],"a":[7,31,80,135,169,216,220,237,295,323],"great":[8,32],"deal":[9,62],"of":[10,21,44,54,72,82,147,181,260,281],"attention":[11],"in":[12,15,25,128,144,195,203,214,253,294],"information":[13,38],"technology":[14,24],"recent":[16],"years.":[17],"The":[18,40],"rapid":[19],"progress":[20],"computer":[22],"hardware":[23,55],"the":[26,35,52,70,124,129,145,164,179,192,212,224,258,276,288,302],"past":[27],"three":[28,131],"decades":[29],"provides":[30],"enhancement":[33],"to":[34,61,92,100,123,198,229,255,301],"database":[36],"industry.":[39],"size":[41],"complexity":[43],"real":[45,208],"world":[46,209],"data":[47,65,77,125,142,213],"is":[48,218],"dramatically":[49],"increasing":[50],"with":[51,63,332],"growth":[53],"technology.":[56],"Although":[57],"new":[58,238],"efficient":[59],"algorithms":[60,89,97],"such":[64,94,215],"are":[66,90,98],"constantly":[67],"being":[68],"proposed,":[69],"large":[73,338],"scale":[74],"high":[75,340],"dimensional":[76],"still":[78],"presents":[79],"lot":[81],"challenges.":[83,95],"In":[84,227,299,316],"this":[85],"dissertation,":[86],"several":[87],"novel":[88,136,324],"proposed":[91,268],"handle":[93],"These":[96],"applied":[99],"domains":[101],"as":[102,104,312],"diverse":[103],"electrocardiography":[105],"(ECG),":[106],"stock":[107],"market":[108],"data,":[109,111,114,116,118],"geospatial":[110],"power":[112],"supply":[113],"audio":[115],"image":[117],"etc.":[119,368],"This":[120],"dissertation":[121],"contributes":[122],"community":[127],"following":[130],"ways:Firstly,":[132],"we":[133,234,266,286,304,320],"propose":[134],"algorithm":[137,311,327,336],"for":[138,155,206,249,328,355],"clustering":[139,158,176,180,225,250],"time":[140,156,174,183,193,251],"series":[141,157,175,184,194],"efficiently":[143],"presence":[146],"noise":[148],"or":[149,243],"extraneous":[150],"data.":[151,167],"Most":[152],"existing":[153],"methods":[154],"rely":[159],"on":[160,173],"distances":[161],"calculated":[162],"from":[163],"entire":[165],"raw":[166],"As":[168],"consequence,":[170],"most":[171],"work":[172],"only":[177],"considers":[178],"individual":[182,187],"\"behaviors,\"":[185],"e.g.,":[186],"heart":[188],"beats":[189],"contrives":[191],"some":[196],"way":[197,217],"make":[199,263,352],"them":[200],"all":[201],"equal":[202],"length.":[204],"However,":[205],"any":[207],"problem,":[210],"formatting":[211],"often":[219],"harder":[221],"task":[222],"than":[223],"itself.":[226],"order":[228,254],"remove":[230],"these":[231],"unrealistic":[232],"assumptions,":[233],"have":[235,267,321],"developed":[236,322],"primitive":[239],"called":[240],"unsupervised":[241,277],"shapelet":[242,278],"u-shapelet":[244,261,309],"shown":[246],"its":[247],"utility":[248],"series.Secondly,":[252],"speed":[256,274],"up":[257,275],"it":[264,292,353],"scalable":[265],"two":[269,289],"optimization":[270,290],"techniques":[271],"which":[272],"can":[273,305],"independently":[280],"each":[282],"other.":[283],"Moreover,":[284],"if":[285],"combine":[287],"procedures,":[291],"results":[293],"super":[296],"linear":[297],"speedup.":[298],"addition":[300],"above,":[303],"also":[306],"cast":[307],"our":[308],"an":[313],"anytime":[314],"algorithm.":[315],"my":[317],"final":[318],"contribution,":[319],"robust":[326],"mice":[330,343],"vocalizations":[331],"symbolized":[333],"representation.":[334],"Our":[335],"processes":[337],"scale,":[339],"dimensional,":[341],"noisy":[342],"vocalization":[344],"by":[345],"dimensionality":[346],"reduction":[347,350],"cardinality":[349],"suitable":[354],"like":[358],"classification,":[359],"clustering,":[360],"similarity":[361],"search,":[362],"motif":[363],"discovery,":[364],"contrast":[365],"set":[366]},"counts_by_year":[],"updated_date":"2026-08-25T07:29:55.448023","created_date":"2016-06-24T00:00:00"}
