c ○ 2001 Kluwer Academic Publishers. Manufactured in The Ne(10)
发布时间:2021-06-07
发布时间:2021-06-07
Abstract. A data cube is a popular organization for summary data. A cube is simply a multidimensional structure that contains in each cell an aggregate value, i.e., the result of applying an aggregate function to an underlying relation. In practical situat
264´ANDWUBARBARA
effects,allthepossiblek 1-factoreffects,andsoonuptothe1-factoreffectsandthe
ABmean(γ.Forexample,γiAisone-factoreffect,γijistwo-factoreffectwhichshowstheABCdependencywithinthedistributionsoftheassociatedattributesA,B,γijkisthree-factor
effectwhichshowsthedependencywithinthedistributionsoftheassociatedattributes
ABCA,B,C.Notethesizeofpossiblehigh-factoreffectscanbeverylarge(forγijk,thesize
isI×J×K).
ABACADBClogy ijkl=γ+γiA+γjB+γkC+γlD+γij+γik+γil+γjk
BDCDABCABDACDBCDABCD+γjl+γkl+γijk+γijl+γikl+γjkl+γijkl(2)
Therearealsosomelinearconstraintstoovercometheoverparameterization,asshowninEq.(3).
γ.A=γ.B=γ.C=γ.D=0
ABACACCDγiAB=0.=γ.j=γi.=γ.k=···=γ.l
...
ABCDABCDγijk=γij=γiABCD=γ.ABCD=0.kljkl..l(3)whereadot“.”meansthattheparameterhasbeensummedovertheindex(Forexample, 1ABABγi.=J
j=0γij).Inshort,theconstraintsspecifythattheloglinearparameterssumto0overallindices.
Therearetwoapproachestoestimatethemodelcoef cients.Oneistheiterativepropor-tional ttingmethod,basedonsolvingthecorrespondinglikelihoodequations.Thismethodcanalwaysgettheprecisesolutions,butitneedsmanyiterativestepsoverthedata.
Theothermethodistocomputethecoef cientsfromthevaluesdirectly.Thecoef cientscorrespondingtoanygroup-byGareobtainedbysubtractingfromaveragelvalueatgroup-byGallthecoef cientsfromhigherlevelgroup-by-s.Forinstance,Eq.(4)showstheparametersina4-dimensionaltable.
γ=l....
γiA=li... γ.
...
ABγij=lij.. γiA γjB γ.
ABCABACBCγijk=lijk. γij γik γjk γiA γjB γkC γ.
...(4)
wherel....isthegrandmeanoraverage(Notethata“·”denotesanaggregationalongthatdimension.)andli...isthemeanoverallvaluesalongithmemberofdimensionA.Froml....andli...,γiAdenoteshowmuchtheaverageofthevaluesalongithmemberofdimensionAdiffersfromtheoverallaverage.
Sarawagietal.(1998)presentfastcomputationtechniquesthatmakethisapproachfeasibleforlargesets,andalthoughthismethoddoesnotgiveprecisesolutions,itsfaster
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