Anotherpossibilityistoentersomealreadyknownimplicationsbeforestartingtheexploration.Theseimplications,theuseralreadyknowstobevalid,
|
|
|
- Coral Stokes
- 10 years ago
- Views:
Transcription
1 BackgroundImplicationsandExceptions AttributeExplorationwith jectsinaspeciedcontext.thisknowledgerepresentationisespeciallyuseful Summary:Implicationsbetweenattributescanrepresentknowledgeaboutob- TechnischeHochschuleDarmstadt,FachbereichMathematik cspringer-verlagberlin{heidelberg1995 erdstumme offormalconceptanalysisthatsupportstheacquisitionofthisknowledge.fora plicationsbetweenattributesofthiscontexttogetherwithalistofobjectswhich whenitisnotpossibletolistallspeciedobjects.attributeexplorationisatool speciedcontextthisinteractiveproceduredeterminesaminimallistofvalidim- backgroundimplications)andallvalidimplications.thelistofimplicationscan setofimplicationsthatllsthegapbetweenpreviouslygivenimplications(called besimpliedfurtherifexceptionsareallowedfortheimplications. arecounterexamplesforallimplicationsnotvalidinthecontext.thispaperdescribeshowtheexplorationcanbemodiedsuchthatitdeterminesaminimal 1.Introduction speciedcontext.thisknowledgerepresentationisespeciallyusefulwhenit isnotpossibletolistallspeciedobjects.attributeexploration(cf.anter Implicationsbetweenattributescanrepresentknowledgeaboutobjectsina (1987),Wille(1989))isatoolofformalconceptanalysisthatsupportsthe acquisitionofthisknowledge.formalconceptanalysiswasintroducedin Wille(1982)andhasgrownduringthelastfteenyearstoanusefultoolin determinesaminimallistofimplicationsthatissucienttodeduceallvalid dataanalysis. Foraspeciedcontexttheinteractiveprocedureofattributeexploration implicationsbetweentheattributesofthecontexttogetherwithalistof hasalreadysomeideahowtheattributesarerelated.beforestartingthe objectasacounterexample. Usually,theuserdoesnotstartsuchanexplorationfromscratch,buthe objectswhicharecounterexamplesfortheimplicationsthatarenotvalidin usercaneitheracceptasuggestedimplicationorhemustsupplyanew thecontext.asaninteractiveprocedure,theprogramsuggestsimplications thenumberofremainingpossibleimplications. explorationhecanenteralistofobjectswhichmaysignicantlydecrease totheuserwhichdonotcontradicttoalreadygivencounterexamples.the Anotherpossibilityistoentersomealreadyknownimplicationsbeforestartingtheexploration.Theseimplications,theuseralreadyknowstobevalid,
2 InP.Burmeistersimplementation(1987),itisalsopossibletoenterbackgroundimplications.Thisprogramhoweverdeterminesaminimalsetof areherecalledbackgroundimplications.thispaperdescribesageneral- ofimplicationswhich togetherwiththebackgroundimplications is izationofattributeexploration,thatitisabletodetermineaminimallist sucienttodeduceallvalidimplications. thisprogramdoesnotdependinanywayonthebackgroundimplications onestartswith.intheapproachpresentedinthispaperthebackground usedtodecreasethenumberofquestionstotheuser.theresultinglistin implications regardlessofthebackgroundimplications.theyareonly implicationsareusedtominimizeonlythenumberofadditionallyneeded describeacontextbyaminimalnumberofimplications.theremaybemore Inthenextsectionthebasicdenitionsofformalconceptanalysisarerecalledandexplainedbyanexample,beforeimplicationsincontextsand implicationsisdescribed.itisilluminatedbyanexampleinthelastsection, sectiontheinteractiveprocedureofattributeexplorationwithbackground thenotionsofcompletenessandirredundancyareintroduced.inthethird implicationsifsomeofthemareobvious. implications.thisisbasedonthebeliefthatitisnotalwaysthebestto theresultoftheexploration. 2.ImplicationsofContextsandtheL-Duquenne- wherewealsodiscuss,howtheadmissionofexceptionscanfurthersimplify Firstwebrieyrecallthebasicdenitionsofformalconceptanalysis(cf. Denition:Wecall(;M;I)acontext,whereandMaresetsandIis anterandwille(1995))andgiveanexample. arelationbetweenandm(i.e.im).theelementsofandm read:\theobjectghastheattributem". arecalledobjectsandattributes,respectively,andgim(:()(g;m)2i)is uigues-basis ForeverysetAofobjectswedenethesetA0:=fm2MjgIm forallg2agofallattributessharedbyallobjectsina.duallytheset B0:=fg2jgImforallm2Bgisthesetofallobjectshavingall Nowaconceptofthecontext(;M;I)isapair(A;B)withA,BM, attributesinbm. Inmumandsupremumintheconceptlatticearecalculatedasfollows: A0=B,andB0=A.ThesetAiscalledtheextentoftheconcept,theset Btheintent.Thehierarchicalsubconcept-superconcept-relationisgivenby (A1;B1)(A2;B2):()A1A2(()B1B2).Thesetofallconcepts whichiscalledtheconceptlatticeof(;m;i)andisdenotedbyb(;m;i). ofacontext(;m;i)togetherwiththisorderrelationisacompletelattice t2t(at;bt)=(\ ^ t2tat;([ t2tbt)00);_ t2t(at;bt)=(([ t2tat)00;\ t2tbt):
3 malcontextshowninfig.1.itsobjectsarethegraphs1upto18and relationshipscontainedintheunderlyingdatacontext:weconsiderthefor- itsattributesaretenattributesofundirectedgraphs(cf.wilson(1975)): Thefollowingexampleshowshowtheconceptlatticeunfoldstheconceptual connected,disconnected,bipartite,complete,completebipartite,tree,forest, planar,eulerian,hamiltonian.thiscontext(togetherwithalistofimplications)istheresultoftheattributeexplorationthatisdescribedinthe (g),whichisdenedastheconceptwiththesmallestextentcontaining g,islabeledwith\g".dually,foreveryattributemitsattributeconcept extentwhicharelinkedtoitbyadescendingpathanditcontainsallthose (m),whichisdenedastheconceptwiththesmallestintentcontaining bedeterminedinthediagram:aconceptcontainsallthoseobjectsinits m,islabeledwith\m".thentheextentandintentofeveryconceptcan itsextentandtheattributesconnected,complete,eulerian,andhamiltonian attributesinitsintentthatarelinkedtoitbyanascendingpath.the nected),(planar)and(bipartite),andtheotherby(connected),(planar) rightmostconceptinfig.2forexamplehasthegraphs3,14and18in Inthediagramonecanseetwocubesatthetop:oneisspannedby(discon- initsintent. and(bipartite).thisindicatesthatinbothcasesthethreeinvolvedat- Thedominatingpartinthelatticeliesbetween(connected)and(13).It tributesareindependent. isthedirectproductofa6-element\ladder"witha4-element\rectangle", butitcanalsobeseenas4-dimensionalhypercubesthataregluedtogether ateightvertices.theupperoneliesbetween(connected)and(15)and thirdsection. Itissucienttolabelthelinediagramnotwiththecompleteconcepts,but onlywiththeattributesandobjects:foreveryobjectg,itsobjectconcept and18. rian),and(hamiltonian).thisshowsthat,forconnectedgraphs,thefour attributesplanar,bipartite,eulerian,andhamiltonianareindependent. pointismissing itismarkedinthediagramwithalittledotleftof(15). Inthelowerhypercube(between(bipartite)^(connected))and(13)one ThisindicatesthateverycompletebipartiteHamiltonianplanargraphisalso Eulerian.Infactthereexist(uptoisomorphism)onlytwosuchgraphs,13 isspannedby(planar)^(connected),(bipartite)^(connected),(eule- Denition:AnimplicationbetweenattributesinMisapair(X;Y)of implicationx!yisvalidinacontextkifitisrespectedbyeveryobject intent.theimplicationisthencalledanimplicationofthecontextk.an subsetsxandyofm.itisdenotedbyx!yandisread\ximplies MthatrespectsLalsorespectsX!Y. TrespectsasetLofimplicationsifitrespectseveryimplicationinL.An implicationx!yisentailedbyasetlofimplicationsifeverysubsetof Y".AsubsetTofMrespectstheimplicationifX6TorYT.Theset
4 disconnected bipartite completebipartite tree connected planar Eulerian forest 1 Hamiltonian Lemma1AnimplicationX!YisvalidinacontextKifandonlyif 10 Figure1:Contextofgraphs B(K):Inourexamplefcomplete,Euleriang!fHamiltoniangisanimplicationofthecontext,whichcorrespondstotheequality(complete)plicationscanequivalentlybeunderstoodasequalitiesintheV-semilattice betweentheattributesofthecontext,becausethesetofallintentsisexactly thelargestclosuresystemonmthatrespectsalltheseimplications.theim- Thestructureofaconceptlatticeisalreadydescribedbyallimplications YX00.ThenitisalsorespectedbyeveryconceptintentofK lattice. (Eulerian)=(complete)^(Eulerian)^(Hamiltonian)intheconcept 20
5 complete connected disconnected bipartite planar tree forest Hamiltonian Eulerian complete bipartite Figure2:ConceptlatticeofthecontextinFig.1 Someoftheseimplicationsmaybeknowninadvance,andinthispaper theywillbereferredtoasbackgroundimplications.inaformalsenseevery implicationofacontextcanbeabackgroundimplication.thequestionis nowhowtodescribethestructureoftheconceptlatticewithimplications inthemostecientwaywhenbackgroundimplicationsaregiven.weare lookingforaminimallistthatis\llingthegap"betweenthebackground implicationsandallvalidimplications. Denition:LetKbeanitecontextandLasetof(background)implicationsofK.AsetBofimplicationsofKiscalledL-complete,ifevery implicationofkisentailedbyl[b.itiscalledl-irredundantifnoimplicationa!b2bisentailedby(bnfa!bg)[l.al-basisisa L-completeandL-irredundantsetofimplicationsofK.IfLisemptythen Biscalledcomplete,irredundant,andabasis,respectively.AsubsetPof Miscalledpseudo-intentofKifP6=P00andifforeverypseudo-intentQ withqptheinclusionq00pholds.
6 J.-L.uiguesandV.Duquenne(1986)showthatB:=fP!P00jPisa backgroundimplicationsisdenotedbyp7!p:=p[pl[pll[:::with Duquenne-uigues-basis.WeobtainaL-basisbygeneralizingthisdenition. pseudointentgisabasisof(;m;i)ifmisnite.thisbasisiscalled XL:=X[SfBMjAX;A!B2Lg. willbenite. Denition:TheclosureoperatoronthesetMofattributesinducedbythe foreveryl-pseudo-intentqwithqp,theinclusionq00pholds. AsubsetPofMiscalledL-pseudo-intentofKifP=P6=P00andif, Withoutfurthermentioning,allsetsofattributesconsideredinthefollowing ThesetBL:=fP!P00jPisaL-pseudo-intentgofimplicationsiscalled Proof.Obviously,allimplicationsinBLareimplicationsofK.Weprove L-Duquenne-uigues-basis. Theorem2BLisaL-basisofK. LetP!P002BL.WeshowthatP!P00isnotentailedby(BLn fp!p00g)[lbecauseprespectsallimplicationsin(blnfp!p00g)[l ThisisacontradictionbecauseTdoesnotrespectthisimplication. T6=T00.ThenTisaL-pseudo-intentbydenitionandsoT!T002BL. FurthermoreTrespectsQ!Q00foreveryL-pseudo-intentQT.Suppose thatblisl-completebyshowingthateverysubsettmrespectingall (andprovesothatblisl-irredundant):asp=p,itclearlyrespectsall implicationsinl[blisanintent:ast!tisentailedbylwehavet=t. Duquenne-uigues-basisisB=fcd!abcd;b!ab;ad!abcd;ac!abcdg.For A!A.ThecontextinFig.3showsthatingeneralthisisnotthecase.The pseudo-intentswithandthendeletingthe(trivial)implicationsoftheform OnemayaskifitispossibletogettheL-pseudo-intentsbyjustclosingthe implicationsinl.forq!q002blnfp!p00gwithqpwehave Q00P,asPisaL-pseudo-intent.HencePrespectsalsoQ!Q00. thebackgroundimplicationl:=fcd!agwegetthel-duquenne-uiguesbasisbl=fb!ab;ad!abcd;ac!abcdgwhilefp!p00jpispseudo-intent acquisitiontoolthatcanbeusedtodeterminetheduquenne-uigues-basis B.anter(1987)presentsattributeexplorationasaninteractiveknowledge 3.AttributeExplorationwithBackgroundImplications isnotl-irredundant. withp6=p00gadditionallycontainsacd!abcd.ingeneraltheresultingset ofacontextthatiseithertoolargeforacompleteinputintothecomputer orthatisevennotcompletelyknown.itisbasedonhisnext-closure- Algorithmthatecientlycalculatesclosuresystems.
7 1 2 3abcd TheAttributeExplorationprocedurecanbemodiedsuchthatitcanbe 4 setlofbackgroundimplications.thereforeweproceedsimilartoanter closuresystemonthesetmofallattributes: usedtodetermineinteractivelythel-duquenne-uigues-basisforagiven (1987).FirstweshowthatthesetofallintentsandL-pseudo-intentsisa Figure3: c Lemma3Let(;M;I)beacontext,letLbeasetofimplicationsof andq6p.thenp\qisanintent. (;M;I),andletPandQbeintentsorL-pseudo-intentswithP6Q Proof.PasQandthereforealsoP\QrespectallimplicationsinL[BL exceptp!p00andq!q00.becauseofp6p\qandq6p\qthe ofsimplicityweassumethatm:=f1;:::;ng. NextweintroducethelecticalorderonthesetofsubsetsofM.Forthesake setp\qrespectstheseimplications,too.henceitmustbeanintent. Corollary4ThesetofallintentsandL-pseudo-intentsofanitecontext A<B:()(9i2BnA:A\f1;:::;i?1g=B\f1;:::;i?1g)for Denition:ThelecticalorderonP(M)isdenedby (;M;I)isaclosuresystemonM;withtheclosureoperatorX7!X:= A;BM. X[X[X[:::,whereX:=X[SfBMjA!B2BL;AXg. ForA;BMandi2Mwedene A<iB:()(i2BnAandA\f1;:::;i?1g=B\f1;:::;i?1g) subsetbmthelecticallynextintentorl-pseudo-intentisthesetbi, andai:=((a\f1;:::;i?1g)\fig). lastintentorl-pseudo-intentism. whereiisthemaximalelementinmnbwithb<ibi.thelectically Theorem5ThelecticallyrstintentorL-pseudo-intentis;.Foragiven TheNext-Closure-algorithmofB.anter(1987)listsallclosedsetsofa closuresystemonanitesetinthelecticalorder.inthenexttheoremitis appliedtotheclosuresystemofallintentsandalll-pseudo-intents: a 1 b 2 d 3 4
8 ThistheoremprovidesthecentralpartoftheAttributeExplorationwith backgroundimplications,whichwedescribenow:wewanttodetermine thel-duquenne-uigues-basisofacontext(;m;i)(whichisapriorinot Hofobjects.ThesetHmayalsobeempty. completelygiven)forasetlofbackgroundimplicationsofthecontext. Thealgorithmstartswithapartialcontext(H;M;I\(HM)forasubset Algorithm.Setk:=1andBL:=;. (1)DeterminethekthL-pseudo-intentPkof(H;M;I\(HM)byapplyingTheorem5.IfMisreached(asintent)thenSTOP.BListhen thel-duquenne-uigues-basis. (2)Asktheuser:\IstheimplicationPk!P00 (3)otostep(1). {Iftheansweris\No",thenaskforanobjectgthatdoesnot {Iftheansweris\Yes",thenaddPk!P00 respectthisimplication.therowfgg0thenalsohastobeentered bytheuser.addgtoh. kvalid?" ktoblandincreasek. lecticallyrstl-pseudo-intentsof(h;m;j).let(;m;i)beanitecontext Theorem6Let(H;M;J)beanitecontextandletP1,:::,Pkbethek changewhenanobjectisaddedthatrespectsallpreviouslyacceptedimplicationsandallbackgroundimplications: ThealgorithmiscorrectbecausethelecticallyrstL-pseudo-intentsdonot L-pseudo-intent. PJJ L-pseudo-intentsof(;M;I). fggirespectsallpi!pjj withhandj=i\(hm),inwhichalltheimplicationsinl[fpi! Proof.Fori=1;:::;kwehavePII islecticallylessthanpj,theassertionisaconsequenceofthedenitionof iji=1;:::;kgarevalid.thenp1,:::,pkarealsotheklecticallyrst i.aseveryl-pseudo-intentqpjof(h;m;j) i=pjj ibecauseforeveryg2theset setm:=fconnected(conn),disconnected(disc),bipartite(bip),complete InthissectionweseehowthecontextinFig.1isproduced.Westartwiththe Eulerian(eul),Hamiltonian(ham)gofattributesandwanttoknowwhich (comp),completebipartite(cbip),tree(tree),forest(for),planar(plan), 4.AnExplorationofraphs Astheclassofundirectedgraphscontainsinnitelymanyisomorphism implicationsbetweentheseattributesarevalidforallundirectedgraphs. classes,thereisnopossibilitytodeterminetheduquenne-uigues-basisfor
9 Furthermoreweknowthatatreeisjustdenedasaconnectedforestand thatacompletebipartitegraphisalwaysbipartite.thisjustiesthefollowingbackgroundimplications: Whenwelookatthelistofattributesthenweseethatconnectedanddisconnectedarecontradictingeachother(i.e.,nographcanhavebothattributes). graphs.thegraphstheusergivesascounterexamplesduringtheattribute explorationarejustthesetypicalgraphs. theinnitecontext(;m;i)directly.onehastoworkwithsome\typical" anemptyseth.inthefollowingtheattributesappearinginthepremiseof Wedonothaveanyobjectsatthebeginning,sotheexplorationstartswith animplicationwillnotbelistedintheconclusionagain. fconn,discg!m fconn,forg!ftreeg fcbipg!fbipg ftreeg!fconn,forg TherstL-pseudo-intentistheemptyset.Thereforethedialoguestarts NowHcontainstheobject1.InthisenlargedcontexttherstL-pseudointentisstilltheemptyset,butonthisstepwehave;00=fconn,plan, withthequestion: hamg.q:is;!fconn,plan,hamgvalid? Q:Is;!Mvalid? A:No.1hastheattributesconn,plan,ham. 3isaddedtoH.InthecontextwithH=f1;2;3gtheemptysetis 2isaddedtothesetH. Q:Is;!fplangvalid? A:No.2hastheattributesdisc,bip,for,plan. anintent.thenextl-pseudo-intentisfhamg. A:No.3hastheattributesconn,comp,eul,ham. Theimplicationfhamg!fconngisaddedtoBLwhichwasemptyuptonow. Q:Isfhamg!fconngvalid? A:Yes. Q:Isfeulg!fconn,comp,hamgvalid? A:No.4hastheattributesconn,bip,cbip,plan,eul. :::
10 andthefollowingimplicationsareaccepted: Duringtheexplorationthegraphs1to20aregivenascounterexamples fconn,bip,tree,for,plan,hamg!fcomp,cbip,eulg fconn,bip,tree,for,plan,eulg!fcomp,cbip,hamg fconn,comp,eulg!fhamg fdisc,bip,cbipg!ffor,plang fcompg!fconng fhamg!fconng fconn,bip,cbip,plan,hamg!feulg feulg!fconng fconn,bip,compg!fcbip,tree,for,plang fforg!fbip,plang ThesetenimplicationsconstitutetheL-Duquenne-uigues-basisBL.Every implicationthatisvalidinthecontextcanbededucedfromthemandthe fourbackgroundimplications.theduquenne-uigues-basisconsistsofall andthefourthbackgroundimplicationandthetwoimplicationsftreeg! implicationsinthel-duquenne-uigues-basisandadditionallyoftherst fconn,bip,for,plangandfconn,bip,for,plang!ftreeg.inthisexample thecardinalityofthel-duquenne-uigues-basisisjustthedierenceofthe asexceptionsthatcontradictanimplicationandare(uptoisomorphism) andkeepinmindtheexceptions.forexamplewecanregardallgraphs cases,wemayconrmsomeimplicationsthataretrueforalmostallgraphs Duringtheexplorationtherearesomeimplicationsthatcanbedeniedby cardinalitiesoftheduquenne-uigues-basis,butingeneralitmaybelarger. Thebeginningoftheexplorationdialogueremainsunchanged.Therstdifferenceappearswiththequestion:\Isfcompg!fconn,eul,hamgvalid?", because6is(uptoisomorphism)theonlyconnectedcompleteplanarhamiltoniangraphwhichisnoteulerianandisthereforenotallowedascounterexample.howevertheimplicationhastobedenied:raph06infig.4 servesasnewcounterexample.thenextsuggestionfcompg!fconn,hamg fconn,eul,hamg!fcompg,because14isanexceptioninthesensedened of14thegraph014willbeusedasacounterexamplefortheimplication willbeacceptedwiththeexception7,whichistheonlycompletegraph thatisnothamiltonian.inthiswaytheexplorationcontinues.instead above. ThisapproachyieldsthefollowinglistofimplicationsthatisaL-basisfor allgraphsexceptfor6,7,13,14,and18.behindeveryimplication uniqueinhavingexactlytheirattributes. thegeneralstructureofgraphtheorywithoutbotheringwithpathological onlyonecounterexample(uptoisomorphism).ifwewanttodetermine arelisteditsexceptions.
11 disconnected bipartite completebipartite tree connected planar Eulerian forest Figure4:Additionalgraphsfortheexplorationwithexceptions fdisc,bip,cbipg!ffor,plang fcompg!fconn,eul,hamg fhamg!fconng feulg!fconng fforg!fbip,plang (6;7) 14 Hamiltonian completeplanarhamiltoniangraphs:14and18.that18istheonly fconn,bip,tree,for,plan,hamg!m fconn,bip,tree,for,plan,eulg!m fconn,comp,plan,eul,hamg!m (14;18) HamiltoniantreeandtheonlyEuleriantreeisexpressedby7thresp.8th The6thimplicationindicatesthatthereexist(uptoisomorphism)onlytwo fconn,bip,cbip,plan,hamg!feulg fconn,bip,comp,eul,hamg!m (13;18) implication.itisalsotheonlyhamiltonianbipartitecompletegraph(10th implication). (18) TheresultingconceptlatticeisshowninFig.5.Theimplicationsvalidin 7,13,14,and18.Theconceptlatticeoftheseexceptionsisshown thislatticeareexactlythosewhicharevalidforallgraphsexceptfor6, infig.6.inparticularonecanseeinthediagramthatallexceptionsare connectedplanargraphs. atedbysomeconceptsisinteractivelydetermined. AttributeexplorationdeterminestheV-semilatticethatisgeneratedbythe attributeconcepts.inpremiseandconclusiononlyconjunctionsofattributes areallowed.disjunctionsbecomeinvolvedindistributiveconceptexploration (cf.stumme(1995)),wherethecompletedistributivelatticethatisgener- 6
12 disconnected bipartite connected planar 11 9 complete bipartite Eulerian Hamiltonian 10 complete Figure5:Conceptlatticeresultingoftheexplorationwithexceptions 1 forest 4 17 tree 20 planar Figure6:Conceptlatticeoftheexceptions connected complete bipartite Hamiltonian complete bipartite forest Eulerian tree disconnected
13 Kontexte.TechnischeHochschuleDarmstadt(Latestversion1995forAtariST References: Burmeister,P.(1987):ProgrammzurformalenBegrisanalyseeinwertiger anter,b.(1987):algorithmenzurbegrisanalyse.in:b.anter,r.wille,k. informativesresultantd'untableaudedonneesbinaires.math.sci.humaines andmsdos) E.Wol(Eds.):BeitragezurBegrisanalyse.B.I.-Wissenschaftsverlag,Mannheim,Wien,Zurich,241{25grisverbandausgewahlterraphen.Mittelseminar,THDarmstadt Duquenne,V.anduigues,J.-L.(1986):Famillesminimalesd'implications 95,5{18 Ehrenberger,P.,Heiss,R.,Ihringer,Cl.,andVogel,N.(1992):Be- Wille,R.(1982):Restructuringlatticetheory:Anapproachbasedonhierarchiesofconcepts.In:I.Rival(Ed.):Orderedsets.Reidel,Dordrecht{Boston, anter,b.andwille,r.(1995):formalebegrisanalyse:mathematische acquisitioninformalconceptanalysis.(inpreparation) Stumme,.(1995):DistributiveConceptExploration atoolforknowledge rundlagen.springer,berlin,heidelberg(toappear) 445{470 In:E.Diday(Ed.):Dataanalysis,learningsymbolicandnumericknowledge. NovaSciencePublisher,NewYork,Budapest,365{380 Wilson,R.J.(1975):Introductiontographtheory.Longman,London Wille,R.(1989):Knowledgeacquisitionbymethodsofformalconceptanalysis.
V. Adamchik 1. Graph Theory. Victor Adamchik. Fall of 2005
V. Adamchik 1 Graph Theory Victor Adamchik Fall of 2005 Plan 1. Basic Vocabulary 2. Regular graph 3. Connectivity 4. Representing Graphs Introduction A.Aho and J.Ulman acknowledge that Fundamentally, computer
Path Querying on Graph Databases
Path Querying on Graph Databases Jelle Hellings Hasselt University and transnational University of Limburg 1/38 Overview Graph Databases Motivation Walk Logic Relations with FO and MSO Relations with CTL
MATHEMATICS Unit Decision 1
General Certificate of Education January 2007 Advanced Subsidiary Examination MATHEMATICS Unit Decision 1 MD01 Tuesday 16 January 2007 9.00 am to 10.30 am For this paper you must have: an 8-page answer
Decision Mathematics D1 Advanced/Advanced Subsidiary. Tuesday 5 June 2007 Afternoon Time: 1 hour 30 minutes
Paper Reference(s) 6689/01 Edexcel GCE Decision Mathematics D1 Advanced/Advanced Subsidiary Tuesday 5 June 2007 Afternoon Time: 1 hour 30 minutes Materials required for examination Nil Items included with
Answer: (a) Since we cannot repeat men on the committee, and the order we select them in does not matter, ( )
1. (Chapter 1 supplementary, problem 7): There are 12 men at a dance. (a) In how many ways can eight of them be selected to form a cleanup crew? (b) How many ways are there to pair off eight women at the
Social Media Mining. Graph Essentials
Graph Essentials Graph Basics Measures Graph and Essentials Metrics 2 2 Nodes and Edges A network is a graph nodes, actors, or vertices (plural of vertex) Connections, edges or ties Edge Node Measures
Outline. NP-completeness. When is a problem easy? When is a problem hard? Today. Euler Circuits
Outline NP-completeness Examples of Easy vs. Hard problems Euler circuit vs. Hamiltonian circuit Shortest Path vs. Longest Path 2-pairs sum vs. general Subset Sum Reducing one problem to another Clique
Graph Theory Problems and Solutions
raph Theory Problems and Solutions Tom Davis [email protected] http://www.geometer.org/mathcircles November, 005 Problems. Prove that the sum of the degrees of the vertices of any finite graph is
Network/Graph Theory. What is a Network? What is network theory? Graph-based representations. Friendship Network. What makes a problem graph-like?
What is a Network? Network/Graph Theory Network = graph Informally a graph is a set of nodes joined by a set of lines or arrows. 1 1 2 3 2 3 4 5 6 4 5 6 Graph-based representations Representing a problem
On the Number of Planar Orientations with Prescribed Degrees
On the Number of Planar Orientations with Prescribed Degrees Stefan Felsner Florian Zickfeld Technische Universität Berlin, Fachbereich Mathematik Straße des 7. Juni 6, 06 Berlin, Germany {felsner,zickfeld}@math.tu-berlin.de
CS311H. Prof: Peter Stone. Department of Computer Science The University of Texas at Austin
CS311H Prof: Department of Computer Science The University of Texas at Austin Good Morning, Colleagues Good Morning, Colleagues Are there any questions? Logistics Class survey Logistics Class survey Homework
1.1. The Goal of Clustering
BoundedClustering{ FindingGoodBoundsonClusteredLightTransport MarcStamminger,PhilippSlusallek,andHans-PeterSeidel ComputerGraphicsGroup,UniversityofErlangen fstamminger,slusallek,[email protected]
Handout #Ch7 San Skulrattanakulchai Gustavus Adolphus College Dec 6, 2010. Chapter 7: Digraphs
MCS-236: Graph Theory Handout #Ch7 San Skulrattanakulchai Gustavus Adolphus College Dec 6, 2010 Chapter 7: Digraphs Strong Digraphs Definitions. A digraph is an ordered pair (V, E), where V is the set
On the k-path cover problem for cacti
On the k-path cover problem for cacti Zemin Jin and Xueliang Li Center for Combinatorics and LPMC Nankai University Tianjin 300071, P.R. China [email protected], [email protected] Abstract In this paper we
Linear Coding of non-linear Hierarchies. Revitalization of an Ancient Classification Method
: Revitalization of an Ancient Classification Method Institute of Language and Information University of Düsseldorf [email protected] GfKl 2008 The Problem: Sometimes we are forced to order things
MASTER OF SCIENCE EDUCATION
MASTER OF SCIENCE EDUCATION Rationale Appraisal of the MASE & MAME Programs as an integral part of its improvement was conducted by a group of faculty members from the different concerned colleges. A summary
3. Eulerian and Hamiltonian Graphs
3. Eulerian and Hamiltonian Graphs There are many games and puzzles which can be analysed by graph theoretic concepts. In fact, the two early discoveries which led to the existence of graphs arose from
Quantum Monte Carlo and the negative sign problem
Quantum Monte Carlo and the negative sign problem or how to earn one million dollar Matthias Troyer, ETH Zürich Uwe-Jens Wiese, Universität Bern Complexity of many particle problems Classical 1 particle:
Introduction to Graph Theory
Introduction to Graph Theory Allen Dickson October 2006 1 The Königsberg Bridge Problem The city of Königsberg was located on the Pregel river in Prussia. The river divided the city into four separate
Lecture Notes on GRAPH THEORY Tero Harju
Lecture Notes on GRAPH THEORY Tero Harju Department of Mathematics University of Turku FIN-20014 Turku, Finland e-mail: [email protected] 1994 2011 Contents 1 Introduction..........................................................
Mathematics for Algorithm and System Analysis
Mathematics for Algorithm and System Analysis for students of computer and computational science Edward A. Bender S. Gill Williamson c Edward A. Bender & S. Gill Williamson 2005. All rights reserved. Preface
Graph Theory: Penn State Math 485 Lecture Notes. Christopher Griffin 2011-2012
Graph Theory: Penn State Math 485 Lecture Notes Version 1.4..1 Christopher Griffin 011-01 Licensed under a Creative Commons Attribution-Noncommercial-Share Alike.0 United States License With Contributions
Graph Theory Lecture 3: Sum of Degrees Formulas, Planar Graphs, and Euler s Theorem Spring 2014 Morgan Schreffler Office: POT 902
Graph Theory Lecture 3: Sum of Degrees Formulas, Planar Graphs, and Euler s Theorem Spring 2014 Morgan Schreffler Office: POT 902 http://www.ms.uky.edu/~mschreffler Different Graphs, Similar Properties
WOLLONGONG COLLEGE AUSTRALIA. Diploma in Information Technology
First Name: Family Name: Student Number: Class/Tutorial: WOLLONGONG COLLEGE AUSTRALIA A College of the University of Wollongong Diploma in Information Technology Final Examination Spring Session 2008 WUCT121
Discrete Math for Computer Science Students
Discrete Math for Computer Science Students Ken Bogart Dept. of Mathematics Dartmouth College Scot Drysdale Dept. of Computer Science Dartmouth College Cliff Stein Dept. of Industrial Engineering and Operations
measured (empirical) data from CCC. The modelled values are of value for all (ecosystem specific modelling work deposition)
TABLE 1. PLANNED ACTIVITIES CONCERNING ACIDIFICATION AND EUTROPHICATION DELIVERABLES WGE DELIVERABLES EMEP - Approved Eulerian modelling results - Ecosystem specific depositions - IAM as basis for negotiations
A Study of Sufficient Conditions for Hamiltonian Cycles
DeLeon 1 A Study of Sufficient Conditions for Hamiltonian Cycles Melissa DeLeon Department of Mathematics and Computer Science Seton Hall University South Orange, New Jersey 07079, U.S.A. ABSTRACT A graph
Math 179: Graph Theory
Math 179: Graph Theory Evan Chen May 17, 2015 Notes for the course M179: Introduction to Graph Theory, instructed by Wasin So. 1 1 January 23, 2013 1.1 Logistics Website: www.math.sjsu.edu/~so contains
Network Metrics, Planar Graphs, and Software Tools. Based on materials by Lala Adamic, UMichigan
Network Metrics, Planar Graphs, and Software Tools Based on materials by Lala Adamic, UMichigan Network Metrics: Bowtie Model of the Web n The Web is a directed graph: n webpages link to other webpages
IE 680 Special Topics in Production Systems: Networks, Routing and Logistics*
IE 680 Special Topics in Production Systems: Networks, Routing and Logistics* Rakesh Nagi Department of Industrial Engineering University at Buffalo (SUNY) *Lecture notes from Network Flows by Ahuja, Magnanti
A 2-factor in which each cycle has long length in claw-free graphs
A -factor in which each cycle has long length in claw-free graphs Roman Čada Shuya Chiba Kiyoshi Yoshimoto 3 Department of Mathematics University of West Bohemia and Institute of Theoretical Computer Science
DEPARTMENT OF INFORMATION TECHNOLOGY SEMESTER: IV
NATIONAL INSTITUTE OF TECHNOLOGY RAIPUR DEPARTMENT OF INFORMATION TECHNOLOGY SEMESTER: IV S.No. Board of Studies Sub.Code Subject Name Periods/week Examination Scheme L T P TA FE SE T.C.A. ESE Total Marks
Mathematical Problem Solving for Elementary School Teachers. Dennis E. White
Mathematical Problem Solving for Elementary School Teachers Dennis E. White April 15, 2013 ii Copyright Copyright c 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, Dennis White, University of Minnesota.
Discrete Mathematics Problems
Discrete Mathematics Problems William F. Klostermeyer School of Computing University of North Florida Jacksonville, FL 32224 E-mail: [email protected] Contents 0 Preface 3 1 Logic 5 1.1 Basics...............................
Transportation Polytopes: a Twenty year Update
Transportation Polytopes: a Twenty year Update Jesús Antonio De Loera University of California, Davis Based on various papers joint with R. Hemmecke, E.Kim, F. Liu, U. Rothblum, F. Santos, S. Onn, R. Yoshida,
13 Reading Device/PLC from Database
13 Reading Device/PLC from Database 13.1 Try to Read Device/PLC Data from Database...13-2 13.2 Setting Guide...13-26 13-1 13.1 Try to Read Device/PLC Data from Database [Action Example] Detect the rising
8.1 Min Degree Spanning Tree
CS880: Approximations Algorithms Scribe: Siddharth Barman Lecturer: Shuchi Chawla Topic: Min Degree Spanning Tree Date: 02/15/07 In this lecture we give a local search based algorithm for the Min Degree
Graph Classification and Easy Reliability Polynomials
Mathematical Assoc. of America American Mathematical Monthly 121:1 November 18, 2014 1:11 a.m. AMM.tex page 1 Graph Classification and Easy Reliability Polynomials Pablo Romero and Gerardo Rubino Abstract.
A Fast Algorithm For Finding Hamilton Cycles
A Fast Algorithm For Finding Hamilton Cycles by Andrew Chalaturnyk A thesis presented to the University of Manitoba in partial fulfillment of the requirements for the degree of Masters of Science in Computer
Notes on NP Completeness
Notes on NP Completeness Rich Schwartz November 10, 2013 1 Overview Here are some notes which I wrote to try to understand what NP completeness means. Most of these notes are taken from Appendix B in Douglas
This means that any user from the testing domain can now logon to Cognos 8 (and therefore Controller 8 etc.).
ChaseReferrals and multidomaintrees Graphical explanation of the difference Imagine your Active Directory network looked as follows: Then imagine that you have installed your Controller report server inside
Krishna Institute of Engineering & Technology, Ghaziabad Department of Computer Application MCA-213 : DATA STRUCTURES USING C
Tutorial#1 Q 1:- Explain the terms data, elementary item, entity, primary key, domain, attribute and information? Also give examples in support of your answer? Q 2:- What is a Data Type? Differentiate
New Approach of Computing Data Cubes in Data Warehousing
International Journal of Information & Computation Technology. ISSN 0974-2239 Volume 4, Number 14 (2014), pp. 1411-1417 International Research Publications House http://www. irphouse.com New Approach of
Exercises of Discrete Mathematics
Exercises of Discrete Mathematics Updated: February 4, 2011 Note for the students: the proposed solutions of some exercises are quite lengthy. This does not necessarily mean that the exercise is difficult:
COPYRIGHTED MATERIAL. Contents. List of Figures. Acknowledgments
Contents List of Figures Foreword Preface xxv xxiii xv Acknowledgments xxix Chapter 1 Fraud: Detection, Prevention, and Analytics! 1 Introduction 2 Fraud! 2 Fraud Detection and Prevention 10 Big Data for
Computer Algorithms. NP-Complete Problems. CISC 4080 Yanjun Li
Computer Algorithms NP-Complete Problems NP-completeness The quest for efficient algorithms is about finding clever ways to bypass the process of exhaustive search, using clues from the input in order
Open Problems in Quantum Information Processing. John Watrous Department of Computer Science University of Calgary
Open Problems in Quantum Information Processing John Watrous Department of Computer Science University of Calgary #1 Open Problem Find new quantum algorithms. Existing algorithms: Shor s Algorithm (+ extensions)
Lavastorm Analytic Library Predictive and Statistical Analytics Node Pack FAQs
1.1 Introduction Lavastorm Analytic Library Predictive and Statistical Analytics Node Pack FAQs For brevity, the Lavastorm Analytics Library (LAL) Predictive and Statistical Analytics Node Pack will be
Factor Models for Gender Prediction Based on E-commerce Data
Factor Models for Gender Prediction Based on E-commerce Data Data Mining Competition PAKDD 2015, HoChiMinh City, Vietnam Outline Hierarchical Basket Model Modeling Autocorrelation Sequential Block Voting
Seavus Group 2013. All rights reserved www.seavusprojectviewer.com
Table of Contents: I Microsoft Office 2013 Style... 1 II Microsoft SharePoint Integration... 1 II.1 Connect to the EPM environment... 2 II.2 Import project plans... 2 II.3 Import Master project plans...
CGMgraph/CGMlib: Implementing and Testing CGM Graph Algorithms on PC Clusters
CGMgraph/CGMlib: Implementing and Testing CGM Graph Algorithms on PC Clusters Albert Chan and Frank Dehne School of Computer Science, Carleton University, Ottawa, Canada http://www.scs.carleton.ca/ achan
John Engbers Curriculum Vitae
John Engbers Curriculum Vitae Department of Mathematics, Statistics and Computer Science Work: 414.288.6880 Marquette University Fax: 414.288.5472 340 Cudahy Hall Email: [email protected] Milwaukee,
The Butterfly, Cube-Connected-Cycles and Benes Networks
The Butterfly, Cube-Connected-Cycles and Benes Networks Michael Lampis [email protected] NTUA The Butterfly, Cube-Connected-Cycles and Benes Networks p.1/16 Introduction Hypercubes are computationally
Lecture 30: NP-Hard Problems [Fa 14]
[I]n his short and broken treatise he provides an eternal example not of laws, or even of method, for there is no method except to be very intelligent, but of intelligence itself swiftly operating the
CIS 700: algorithms for Big Data
CIS 700: algorithms for Big Data Lecture 6: Graph Sketching Slides at http://grigory.us/big-data-class.html Grigory Yaroslavtsev http://grigory.us Sketching Graphs? We know how to sketch vectors: v Mv
CSC 373: Algorithm Design and Analysis Lecture 16
CSC 373: Algorithm Design and Analysis Lecture 16 Allan Borodin February 25, 2013 Some materials are from Stephen Cook s IIT talk and Keven Wayne s slides. 1 / 17 Announcements and Outline Announcements
Total colorings of planar graphs with small maximum degree
Total colorings of planar graphs with small maximum degree Bing Wang 1,, Jian-Liang Wu, Si-Feng Tian 1 Department of Mathematics, Zaozhuang University, Shandong, 77160, China School of Mathematics, Shandong
CROPS: Intelligent sensing and manipulation for sustainable production and harvesting of high valued crops, clever robots for crops.
CROPS GA 246252 www.crops-robots.eu CROPS: Intelligent sensing and manipulation for sustainable production and harvesting of high valued crops, clever robots for crops. The main objective of CROPS is to
Getting Started With Delegated Administration
Getting Started With Delegated Administration Delegated Administration (available with Websense v6.1 Corporate Editions) is a powerful tool for distributing filtering and reporting responsibilities for
HOLES 5.1. INTRODUCTION
HOLES 5.1. INTRODUCTION One of the major open problems in the field of art gallery theorems is to establish a theorem for polygons with holes. A polygon with holes is a polygon P enclosing several other
SUPPORTED ACTIVE DIRECTORY TOPOLOGIES BY LYNC 2013
SUPPORTED ACTIVE DIRECTORY TOPOLOGIES BY LYNC 2013 LYNC SERVER 2013 Lync Server 2013 supports the same Active Directory Domain Services topologies as Microsoft Lync Server 2010 and Microsoft Office Communications
UNIFIED BIJECTIONS FOR MAPS WITH PRESCRIBED DEGREES AND GIRTH
UNIFIED BIJECTIONS FOR MAPS WITH PRESCRIBED DEGREES AND GIRTH OLIVIER BERNARDI AND ÉRIC FUSY Abstract. This article presents unifie bijective constructions for planar maps, with control on the face egrees
The Role of Mathematics in Information Security Education
The Role of Mathematics in Information Security Education Stephen D. Wolthusen 1,2 1 Gjøvik University College, N-2802 Gjøvik, Norway, [email protected] 2 Royal Holloway, University of London, Egham
A Turán Type Problem Concerning the Powers of the Degrees of a Graph
A Turán Type Problem Concerning the Powers of the Degrees of a Graph Yair Caro and Raphael Yuster Department of Mathematics University of Haifa-ORANIM, Tivon 36006, Israel. AMS Subject Classification:
SHARP BOUNDS FOR THE SUM OF THE SQUARES OF THE DEGREES OF A GRAPH
31 Kragujevac J. Math. 25 (2003) 31 49. SHARP BOUNDS FOR THE SUM OF THE SQUARES OF THE DEGREES OF A GRAPH Kinkar Ch. Das Department of Mathematics, Indian Institute of Technology, Kharagpur 721302, W.B.,
How To Create A Graph From A Graph With A Powerpoint Powerpoint Toolbox
Octave routines for network analysis GB June 26, 2013 Contents CONTENTS 0 About this toolbox 6 1 Basic network routines 7 1.1 Basic network theory......................................... 7 1.2 Routines................................................
arxiv:1507.04820v1 [cs.cc] 17 Jul 2015
The Complexity of Switching and FACTS Maximum-Potential-Flow Problems Karsten Lehmann 2,1, Alban Grastien 2,1, and Pascal Van Hentenryck 1,2 arxiv:1507.04820v1 [cs.cc] 17 Jul 2015 1 Artificial Intelligence
Discrete Mathematics & Mathematical Reasoning Chapter 10: Graphs
Discrete Mathematics & Mathematical Reasoning Chapter 10: Graphs Kousha Etessami U. of Edinburgh, UK Kousha Etessami (U. of Edinburgh, UK) Discrete Mathematics (Chapter 6) 1 / 13 Overview Graphs and Graph
Topological Properties
Advanced Computer Architecture Topological Properties Routing Distance: Number of links on route Node degree: Number of channels per node Network diameter: Longest minimum routing distance between any
UPPER BOUNDS ON THE L(2, 1)-LABELING NUMBER OF GRAPHS WITH MAXIMUM DEGREE
UPPER BOUNDS ON THE L(2, 1)-LABELING NUMBER OF GRAPHS WITH MAXIMUM DEGREE ANDREW LUM ADVISOR: DAVID GUICHARD ABSTRACT. L(2,1)-labeling was first defined by Jerrold Griggs [Gr, 1992] as a way to use graphs
Montefiore Portal Quick Reference Guide
Montefiore Portal Quick Reference Guide Montefiore s remote portal allows users to securely access Windows applications, file shares, internal web applications, and more. To use the Portal, you must already
FileZilla: Uploading/Downloading Files to SBI FTP
FileZilla Download and Installation Instructions FileZilla is a free software that uses SourceForge as an installation provider. SourceForge is bundling the FileZilla software with other products that
CWSRF Project Descriptions and Examples for Green Project Reserve
CWSRF Project Descriptions and Examples for Green Project Reserve I. Water Efficiency a. Water efficiency is the use of improved technologies and practices to deliver equal or better services with less
Boulder Dash is NP hard
Boulder Dash is NP hard Marzio De Biasi marziodebiasi [at] gmail [dot] com December 2011 Version 0.01:... now the difficult part: is it NP? Abstract Boulder Dash is a videogame created by Peter Liepa and
Findings from the 9 th Annual MetLife S tudy of Employee Benefits Trends A Blueprint for the New Benefits Economy
UFS Findings from the 9 th Annual MetLife S tudy of Employee Benefits Trends A Blueprint for the New Benefits Economy Ronald Leopold, MD, MBA, MPH National Medical Director, Vice President MetLife U.S.
Using SAS ACCESS to retrieve and store data in relational database management systems
Using SAS ACCESS to retrieve and store data in relational database management systems Department of Biology Dalhousie University SHRUG meeting, February 23rd 2007 Disclaimer Background SAS is the only
DeparTmenT of mathematics 200 Albert W. Brown Building (585) 395-2036; Fax: (585) 395-2304 www.brockport.edu/math Major in Mathematics (46 credits)
Mathematics 315 Department of Mathematics 200 Albert W. Brown Building (585) 395-2036; Fax: (585) 395-2304 www.brockport.edu/math Chair and Associate Professor: Mihail Barbosu, PhD, Paris Observatory and
Topology. Shapefile versus Coverage Views
Topology Defined as the the science and mathematics of relationships used to validate the geometry of vector entities, and for operations such as network tracing and tests of polygon adjacency Longley
Creating a File Geodatabase
Creating a File Geodatabase Updated by Thomas Stieve January 06, 2012 This exercise demonstrates how to create a file geodatabase in ArcGIS 10; how to import existing data into the geodatabase, and how
