时间序列时序关联规则挖掘研究(9)

发布时间:2021-06-06

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seriesdatapre-processing,tothelaststep,theinterpretationandevaluationoftemporalassociationrules.Ineverystep,theauthorcombstheexistingresearch,teststherelativetheoreticalmodels,offersimprovementandprovesit.Becausetheminingofmultivariatetimeseriesisahotissue,theauthordiscussesitinthelast

canpart.Theinnovationsofthedissertationbeincludedasfollows.

(1)Intimeseriesdatapre—processing,theauthorputsforwardsrecognition

onmethodofoutliernoisedatabaseddatavarianceratio.Timeseriesusually

containsnoisedata,whichwillaffecttheminingtemporalassociationrules,SOitshouldbecleanedoutbeforemining.Becausetimeseriescompressionisintoleranttooutliernoisedata,meanwhiletheexistenceofoutlierwillaffectthedivisionoftimeseriesandrepresentationoftimeseriespatterns,SOidentifyinganddeletingtheoutlierintimeserieswillbeoneoftheimportantworksintimeseriesdata

onpre processing.Whether

surroundingdata.The

estimatetheadatumistheoutlier,dependsUSeSitsvibrancywithauthordatavarianceratiooftimeseriesdatatovibrancy,andthenoffersrecognitionofoutliernoisedata.

(2)In

distance

bringstimeseriesandsimilaritymeasure,theauthorcomesupwithEuclidmethodtomeasurethesimilarityoftwodynamictimewarpingmeta—patternsand,andalsoforwarddistancemeanstomeasurethesimilarityoftWOtimeseriespatterns.Inminingtemporalassociationrules,themeta patternmonotonydistancemethod

notsuitableandthemeta-patternvectordistancemethodbothareforgettingfrequentpatternwhenmeasuringthesimilaritybetweentwometapatterns.Aimingthespecialtyoftimeseriespattern,thedissertationoffersweighted

timewarpingdistancemethodofmeta—pattern,andthencomesupwithdynamicmeans,whichCalldistancemeasurethesimilaritybetweentwosequentialpatterns.

(3)In

forwardsthetheacquirementoftemporalassociationrules,theauthorputslayeredmeans.The

rulestimerestrictionoftemporalassociationrulestheand...ofassociationdeterminesdifficulty

andofcanacquiringtemporalassociationrules.Inordertodecreasethedifficulty,wetemporalassociationrulesintodifferentlengthdividethebeforerofSOthenmine,thatiscalledthe

layeredminingoftemporalassociationrules.Becauseofthedifferenceindefiningthefrequentpatterns,themethodisdifferentfromotherminingways.Meanwhilebecausethemethodconsidersthebeforerofdifferentlength,ithastheunique4

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