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The following risks, threats, and vulnerabilities were found in a healthcare IT infrastructure servicing patients with life-threatening situations. Given the list, select which of the seven domains of a typical IT infrastructure is primarily impacted by the risk, threat, or vulnerability. Risk – Threat – Vulnerability Primary Domain Impacted Unauthorized access from pubic Internet Remote…

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Which social groups are marginalised, excluded or silenced in a way? Chronicle of a death foretold is a book written by Gabriel Garcia Marquez published in 1981 talks about the tragic death of the protagonist Santiago Nasar who is brutally killed by Vicar brothers. Marquez explores themes like ritual, honour, fate, memory, the sacred and…

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1. Introduction 2. Johnson & Johnson is headquartered in New Brunswick, New Jersey, the consumer division being located in Skillman, New Jersey. The corporation includes some 250 subsidiary companies with operations in 60 countries and products sold in over 175 countries. Johnson & Johnson had worldwide sales of $70.1 billion during calendar year 2015. The…

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CHAPTERT1 INTRODUCTION DATATMINING DataTMiningTisTtheTmethodTofTextractingTtheTmassiveTamountsTofTknowledge.TItTisTtheTusedTforTlocatingTinformationTlikeTpatterns,Tassociations,TanomaliesTandTsignificantTstructuresTfromThugeTamountsTofTdataTkeptTinTdatabase,TinformationTwarehouses,TorTotherTdataTrepositories.TThisTcanTbeTowingTtoTtheTsupplyTofTgiantTamountsTofTknowledgeTinTelectronicTforms,TandTadditionallyTtheTrequirementTforTmodifyingTtheTdataTintoTusefulTinformationTandTdataTforTbroaderTapplicationsTasTwellTasTmarketTanalysis,TbusinessTanalysis,TandTdataTprocessingThasTattractedTaTgoodTdealTofTattentionTinTdataTtrade. DataTminingThasTbeenTpopularlyTtreatedTasTaTwordTofTinformationTDiscoveryTinTDatabasesT(KDD),TothersTreadTasTaTnecessaryTstepTwithinTtheTprocessTofTinformationTdiscovery.TKnowledgeTdiscoveryTasTaTmethodTconsistsTofTAssociateTinTNursingTunvariedTsequenceTofTtheTsubsequentTsteps: knowledgeTcleaning(toTtakeTawayTnoiseTorTdigressiveTdata),T knowledgeTintegration(whereTmultipleTknowledgeTsourcesTisTalsoTcombined)1T, knowledgeTselection(whereTknowledgeTrelevantTtoTtheTanalysisTtaskTareaTunitTretrievedTfromTtheTdatabase),T knowledgeTtransformation(whereTknowledgeTareaTunitTremodeledTorTconsolidatedTintoTformsTacceptableTforTminingTbyTactivityToutlineTorTaggregationToperations,TforTinstance)T, knowledgeTmining(anTessentialTmethodTwhereverTintelligentTwaysTareaTunitTappliedTsoTasTtoTextractTknowledgeTpatterns),T patternTevaluation(toTdetermineTtheTactuallyTfascinatingTpatternsTrepresentingTdataTsupportedTsomeTpowerfulnessTmeasures;Tand dataTpresentationT(whereTimageTandTdataTillustrationTtechniquesTareaTunitTwontTtoTpresentTtheTwell-minedTdataTtoTtheTuser). DataTMiningTTasks DataTminingTtasksTmayTbeTclassifiedTintoT2TclassesT:TdescriptiveTdataTminingTandTpredictiveTdataTmining. SummarizationTisTthatTtheTgeneralizationTorTabstractionTofTinformation.TATcollectionTofTrelevantTknowledgeTisTabstractedTandTsummarized,TensuingTaTsmallerTsetTwhichTprovidesTaTgeneralTsummaryTofTinformation. ClusteringTisTseggregatingTsimilarTteamsTfromTunstructuredTknowledge.TItTisTtheTtaskTofTclusteringTaTcollectionTofTobjectsTinTanTexceedinglyTsuchTthatTobjectTinTsameTgroupTareTuniqueTandTadditionalTlikeToneTanotherTthanTtoTthoseTinTotherTteams.TOnceTtheTclustersTseggregated,TtheTobjectsTareTtaggedTwithTtheirTcorrespondingTclusters,TandTcustomaryToptionsTofTtheTobjectsTinTclusterTwillTbeTsummarizedTtoTmakeTaTcategoryTdescription. ClassificationTisTlearningTrulesTwhichTwillTbeTappliedTtoTnewTknowledgeTandTcanTusuallyTembodyTfollowingTsteps:TpreprocessingTofTinformation,TplanningTmodeling,TlearningTorTfeatureTchoiceTselectionTandTvalidationT/evaluation.TClassificationTpredictsTcategoricalTcontinuousTvaluedTfunctions.TClassificationTisTthatTtheTderivationTofTmodelTthatTdeterminesTtheTcategoryTofTassociateTdegreeTobjectTsupportedTitsTattributes.TaTcollectionTofTobjectTisTgivenTasTcoachingTsetTduringTwhichTeachTobjectTisTdiagrammaticTbyTvectorTofTattributesTtogetherTwithTitsTcategory.TByTanalyzingTtheTconnectionTbetweenTattributesTandTsophisticationTofTtheTobjectsTwithinTtheTcoachingTset,TclassificationTmodelTmayTbeTmade. RegressionTisTfindingTperformTwithTlowestTerrorTtoTmodelTknowledge.TIt’sTappliedTforTmathematicsTmethodologyTwhichTisTmostTfrequentlyTusedTforTnumericTprediction.TMultivariateTanalysisTisTwidelyTusedTforTpredictionTandTprediction,TwhereverTitThasTsubstantialToverlapTwithTtheTsphereTofTmachineTlearning.TMultivariateTanalysisTisTadditionallyTwillTnotTperceiveTthatTamongTtheTindependentTvariablesTareaTunitTassociatedTwithTtheTvariable,TandTtoTexploreTtheTstylesTofTtheseTrelationships. AssociationTisTcravingTforTrelationshipTbetweenTvariablesTorTobjects.TItTaimsTtoTextractTattention-grabbingTassociation,TcorrelationsTorTcasualTstructuresTamongTtheTobjectsTi.e.TtheTlooksTofTanotherTsetTofTobjects.TTheTassociationTrulesTmayTbeThelpfulTforTselling,TgoodsTmanagement,TadvertisingTetc.TAssociationTruleTlearningTmayTbeTaTwidespreadTandTwellTresearchedTtechniqueTforTlocatingTattention-grabbingTrelationsTbetweenTvariablesTinTmassiveTdatabases. MOTIVATION DataTminingTisTtheToneTofTtheTwayTofThandlingThugeTinformationTforTminingTcompetitors.TWithThugeTamountTofTunstructuredTreviewTdata,TbothTtheTcompetitorTandTcustomerTfacedTtheTcrucialTchallengeTofTextractingTveryTusefulTinformation.TProjectTisTaboutTtheTrecommenderTsystemTforTbothTtheTcustomerTandTtheTcompetitorTbyTinformationTfilteringTsystemTthatTseeksTtoTpredictTtheTratingTorTreviewsTthatTcustomerTprovides.DatasetTisTcollectedTfromTtheTonline.TItTisTaboutTtheTcustomerTreviewTaboutTtheThotel.TPreprocessingTofTdataTisTinvolvedTwhereTirrelevantTdataTareTremovedTandTwithTtheTprocessedTdataTneedTtoTanalyzeTandTidentifyTtheTtopTk-businessTcompetitorsTofTaTparticularTlocationTofTcity.T CustomerTfindsTdifficultiesTtoTchooseTtheTbestThotelTtoTvisitTandTenjoy.TCustomerTcanTfindTtheThotelTreviewsTfromTwebTsearchTresult,TbutTthatTdoesn’tTprovideTproperTinformationTandTthatTleadTtoTconfusionTforTtheTcustomerTtoTchooseTtheThotel.TTheTcompetitor’sTin-orderTtoTmakeTtheTbusinessTcompetitorTlevelThigh,TtheyTgetTtheTfeedbackTfromTtheTcustomerTandTthatThelpsTtoTimproveTtheTnegativeTcommentsTaboutTtheThotel.TTheTmotivationTofTtheTprojectTisTinTorderTtoTovercomeTtheTaboveTproblemTandTmakeTcustomerTtoTprovideTaTclearTdecisionTwithTtheTanalysisTofTreviews.Similarly,TtheThotelTcompetitorsTtoTidentifyTstepsTtoTimproveTservice. OBJECTIVE ToTidentifyTtheThotelTcompetitorsTbasedTonTtheTcustomerTreviewsTtoTbusiness. ToTdetermineTtheTimprovementTofThotelTbusiness. ToTidentifyTtheTfakeTreviewsTbyTunauthorizedTusers. ToTrecommendTtheTbestThotelTtoTtheTcustomers. ORGANIZATIONTOFTTHETTHESIS OrganizationTofTtheTprojectTrepresentsTtheTshortTdescriptionTofTeachTchapter.TChapterT1TprovidesTtheTgeneralTintroductionTtoTdataTmining,TintroductionTtoTtheTprojectTandTdescribesTtheTmotivationTandTobjectiveTofTtheTproject.TChapterT2TisTaboutTtheTLiteratureTSurveyTofTvariousTapproachesTusedTandThowTitTcanTuseTinTidentifyingTtheTbusinessTCompetitorTinTtheTproject.TChapterT3TexplainsTaboutTtheTalgorithmTusedTforTtheTcomponentsTinvolved,TinformationTaboutTtheTtoolTusedTandTtheTdatasetTforTanalysisTpurpose.ChapterT4providesTtheinformationTaboutTtheTimplementationTofTtheTprojectTandTtheTprocessTtoTbeTfollowedTinTorderTtoTachieveTtheTobjectiveTofTprojectTand.TChapterT5TgivesTtheTconclusionTandTfutureTactionTplan. CHAPTERT2 LITREATURETSURVEY TMiningTcompetitor’sTofTaTgivenTitem,TtheTmostTinfluencedTfactorTofTtheTitemTwhichTsatisfiesTtheTcustomerTneedTcanTbeTextractedTfromTtheTdataTthatTisTtypicallyTstoredTinTtheTdatabase.TThisTsectionTgivesTtwoTtypesTofTliteraturesTsuchTasTcompetitorTminingTandTunstructuredTdataTmanagement.TTheTunstructuredTdataTsourcesTareTinTaTdifferentTformat,TwhichTisTnotTfallTunderTanyTpredefinedTcategory.TWhenTmanagingTthousandsTofTcustomers,TbusinessTwillThaveTdifficultyTsustainingTtheTrisingTcostsTcreatedTbyTinteractionsTamongTpeople. 2.1TONLINETREVIEWS: JinTetTalT1,InformationTfromTwebTproducesTtheTcustomerTopinionTinTdifferentTperspective.TEachTcustomerThasTdifferentTopinionsTandTanalysisTofTcompetitorTfromTlargeTwebTinformationTisTdone.TTherefore,ToneTofTtheTbestTcompetitiveTstrategiesTisTtheTsuccessfulTutilizationTofTwebTdataTforTdecisionTsupport. CustomerTreviewsTforTbusinessTcompetitorTminingTisTcollectedTthroughTseveralTmethods,TwhichTisTusuallyTunstructuredTdataT.MostTofTtheTdataTminingTtechnologiesTcanTonlyThandleTstructuredTdata.TSo,TduringTminingTprocess,TunstructuredTdataTisTnotTtakenTintoTaccountTandTmuchTvaluableTserviceTinformationTisTlost.TStructuredTsystemsTareTthoseTwhereTtheTdataTandTtheTcomputingTactivityTisTpredeterminedTandTwell-defined.TUnstructuredTsystemsTareTthoseTthatThaveTnoTpredeterminedTformTorTstructureTandTareTusuallyTfullTofTtextualTdata.TTypicalTunstructuredTdataTincludeTemail,Treports,Tletters,TandTotherTcommunications. 2.2TANALYSISTOFTCOMPETITORSTINTBUSINESS: LappasTetTalT2,CompetitiveTminingTisTdoneTonTdifferentTdomainsTinTorderTtoTgetTanTappropriate.SearchingTtheTqueriesTasTperTtheTcustomerTpreferenceTandTrequestingTtheTsearchTengineTforTtheTmatchingTresults.TFinally,TcustomerTgoesTwithTtheTchoiceTofTtheTsearchTengine.TSometimes,TtheTexactTcustomerTpreferenceTisTnotTidentified,TbutTcustomerTgoesTwithTtheTbestTofTsearchTresultsTobtainedTthatTmatchesTfewTofTpreferences.THowever,TthisTtechniqueTfindsTmanyTproblemsTsuchTasTfindingTtheTtop-nTbusinessTcompetitorsTofTanTitemTandTstructuredTdata. LiTetTalT3,ToaccomplishTminingTcompetitiveTinformationTareTrequiredTsuchTasTaTaboutTtheTcompany,TitsTproductTorTpersonTwhoTworksTinTthatTcompanyTfromTtheTweb.TAnTalgorithmTwasTcalledT”CoMiner”,TalgorithmTextractsTaTsetTofTcomparativeTitemTofTtheTinputTinformationTandTthenTranksTthemTaccordingTtoTtheTtheirTsimilarityTorTidentityTfoundTinTcompartiveness,TandTfinallyTfindsTtheTcompetitiveTitem.TUsuallyTtheTCoMinerTspecificallyTdesignedTforTsupportingTaTparticularTdomain.TTheTdisadvantageTofTCoMinerTisTforTmanyTdomainsTitTwillTbeTdifficultTtoTidentify. TPantTetTalT4,WebTfootprintTrefersTtoTtheTinformationTfromTonlineTmetricsTforTtopTcompetitorTidentification.TFirm’sTwebTsiteTprovidesTtheTcontentTofTfirm’sTactivities,TproductsTandTserviceTtoTitsTvariousTstakeholders.TThisTisTbasedTonTtheTdata,TfirmTlinksTandTwebsiteTinformationTthatTareTstoredTasTlogTtoTidentifyTtheTpresenceTofTonlineTisomorphism,ThereTtheTCompetitiveTisomorphism,TwhichTisTaTofTcompetingTfirmsTbecomingTsimilarTasTtheyTmimicTeachTotherTunderTcommonTmarketTservices.TPredictiveTmodelsTforTcompetitorTidentificationTbasedTonTonlineTmetricsTareTsupportedTthanTtheTofflineTdata.TTheTtechonolgyTjoinsThandsTwithTtheTonlineTandTofflineTmetricsTtoTboostTtheTdevelopingTperformance. SocialTmediaTisTconsideredTasTtheTpopularTinformationTexchangeTplatformTsuchTasTTwitterTandTFacebookTthatTareTbeingTincreasinglyTusedTbyTfirmsTtoTcommunicateTwithTvariousTstakeholders.OnlineTNewsTstoriesTavailableTonTtheTwebTfromTaTlargeTnumberTofTnewsTsourcesTthatTmentionTtheTfirm. ShenghuaTBaoTetTalT5,TAbleTtoTsolveTtheTproblemTofTambiguityTbyTmeansTofTprovidingTtheTinputTentityTwithTadditionalTrestrictions.TCoMinerTisTtheTalgorithmTforTdiscoveringTcompetitors,TtheirTcompetitiveTdomains,TandTdetailedTcompetitiveTevidencesTbyTminingTwebTresources.TCoMinerTextractsTtheTcompetitiveTdomainTinTwhichTtheTgivenTentityTandTitsTcompetitorsTplayTagainstTeachTotherTbyTminingTtheTsalientTphraseTfromTaTsetTofTwebTphrase. 2.3TRATING: LiTetTal6,TRankingTmethodsTtoTgiveTtheTcompetitorTinTaTrantingTmethod.TDataTfromTlocation-basedTsocialTmediaTareTusedTforTrankingTtheTcompetitor.TTheTuseTofTPage-RankTmodelTandTit’sTvariantTtoTobtainTtheTCompetitiveTRankTofTfirms.T HoweverTminingTcompetitorsTfromTtheTsocialTmediaTdevelopedTmanyTprivacyTrelatedTissues.TAlso,TsocialTmediaTinformationTareTnotTalwaysTaccurate,TpredictionTofTcompetitorTmayTleadTtoTincorrectTresult. TaniaTFerreiraTetTalT7,TGatheringTknowledgeTaboutTtheTcustomersTofTe-commerceTplatforms.TAllowTtheTanalysisTofTbehaviors.TFindTpurchasingTpatterns.TDevelopTaTbetterTrelationshipTmanagementTwithTcustomer.TBetterTstockTmanagement.TOptimizingTtheTorganization’sTprocesses.SupportTtoTcreateTmarketingTactions.GreaterTcompetitiveness.BetterTfinancialTperformance. E-commerceTisTaTconceptTapplicableTtoTanyTtypeTofTbusinessTorTtradeTtransactionTthatTallowsTconsumersTtoTtransactTgoodsTandTservicesTelectronicallyTwithoutTpreventTofTtimeTorTdistance.TAdvantagesTofTe-commerceTare:GreaterTconvenienceTinTpurchasingTtheTproductTorTservice,TNoTstandingTinTqueueTorTbeingTplacedTonTholdTevermore,T24-hourTavailability,TAccessTatTanyTtimeTforTdevicesTwithTanTInternetTconnection,TAccessTtoTstoresTlocatedTremotely,TEasierTtoTcompareTprices,TReduceTemployeeTcosts.TDisadvantagesTofTe-commerceTare:TNeedTforTanTInternetTaccessTdeviceTandTconnection,TInabilityTtoTexperienceTtheTproductTbeforeTpurchase,TVulnerabilityTofTconfidentialTdata,TTechnicalTproblems,TPossibleTdelaysTorTproductTdamageTduringTdelivery. 2.4TINFORMATIONTRETRIEVAL: MohamedTRedaBouadjenekTetTalT8,TScienceTthatTdealsTwithTtheTrepresentation,Tstorage,TorganizationTof,TandTaccessTtoTinformationTitemsTinTorderTtoTsatisfyTtheTuserTrequirementsTconcerningTtoTthoseTinformation.TToTimproveTtheTclassicTIRTprocessTandTreduceTtheTamountTofTirrelevantTdocuments:TQueryTreformulationT-TwhichTincludesTexpansionTorTreductionTofTtheTquery,TPost-filteringTorTre-rankingTofTtheTretrievedTdocuments,TImprovementTofTtheTIRTmodelT–TtheTwayTdocumentsTandTqueriesTareTrepresentedTandTmatchedTtoTquantifyTtheirTsimilarities.TQueryTreformulationTisTtheTprocessTwhichTconsistsTofTtransformingTanTinitialTqueryTQTtoTanotherTqueryTQ?.TThisTtransformationTmayTbeTeitherTaTreductionTorTanTexpansion.TQueryTReductionTreducesTtheTqueryTsuchTthatTsuperfluousTinformationTisTremoved,TwhileTQueryTExpansionTisTtoTenhanceTtheTqueryTwithTadditionalTinformationTlikelyTtoToccurTinTrelevantTdocuments. WanTetTalT9,TCompetitivenessTinTtheTcontextTofTproductTdesign.TInitialTstepTisTtheTdefinitionTofTaTdominanceTfunctionTthatTrepresentsTtheTvalueTofTaTproduct.TIdentificationTofTtheTdemandTforTtheTproductTandTprovidingTtheTsameTlevelTinTtheTentireTdomain.TheTgoalTisTthenTtoTuseTtheTfunctionTtoTcreateTitemsTthatTareTnotTdominatedTbyTother,TorTmaximizeTitemsTwithTtheTmaximumTpossibleTdominanceTvalue.TSimilarly,TitTrepresentsTitemsTasTpointsTinTaTmultidimensionalTspaceTandTlooksTforTsubspacesTwhereTtheTappealTofTtheTitemTisTmaximized. 2.5TOPINIONTMINING: Marrese-TayloretTalT10,TOverallTopinionTpolarityTisTcalculatedTandTclassifiedTasTpositiveTorTnegative.TInTsentenceTlevel,TeachTsentenceTinTtheTdocumentTisTanalyzedTandTdeterminesTtheTopinionTexpressedTinTaTsentenceTasTpositive,Tnegative,TorTneutral.TInTopinionTmining,TtheTtermTaspectTmeansTimportantTfeaturesTofTproductsTratedTbyTcustomersT(ForTexample,TinTcaseTofTrestaurantTfood,Tservice,TcleanlinessTetc.).TTheTproductTandTrestaurantTreviewsTareTaTmixtureTofTpositiveTandTnegativeTopinionTaboutTdifferentTaspects.TItTneedsTmoreTfine-grainedTanalysisTofTreviewsTtoTmineTtheseTmixedTopinions,TaspectTlevelTperformTthisTtask.THenceTaspectTbasedTopinionTminingTisTpreferredTinTthisTwork.TTheTcoreTtasksTinTaspectTbasedTopinionTminingTisTaspectTidentification,TaspectTbasedTopinionTwordTidentificationTandTitsTorientationTdetection. VlachouTetTalT11,TTop-kTqueriesTareTwidelyTappliedTforTretrievingTtheTkTmostTinterestingTobjectsTbasedTonTtheTindividualTuserTpreferences.TClearly,TanTobjectT(product)TthatTisThighlyTrankedTbyTmanyTusersT(customers)ThasTobviouslyTaTwiderTvisibilityTandTimpactTinTtheTmarket.TThus,TanTintuitiveTdefinitionTofTtheTinfluenceTofTaTproductTinTtheTmarketTisTtheTnumberTofTcustomersTthatTconsiderTitTappealingT(theTproductTbelongsTtoTtheirTtop-kTresults)TbasedTonTtheirTpreferences.TIdentifyingTtheTmostTinfluentialTobjectsTfromTaTgivenTdatabaseTofTproductsTisTimportantTforTmarketTanalysisTandTdecision-makingTandTisTbeneficialTforTseveralTreal-lifeTapplications. AnaTValdiviaTetTalT11,TSentimentTclassification,TtheTbest-knownTsentimentTanalysisTtask,TaimsTtoTdetectTsentimentsTwithinTaTdocument,TaTsentence,TorTanTaspect.TThisTtaskTcanTbeTdividedTintoTthreeTsteps:TpolarityTdetectionT(labelTtheTsentimentTofTtheTtextTasTpositive,Tnegative,TorTneutral),TaspectTselection/extractionT(obtainTtheTfeaturesTforTstructuringTtheTtext),TclassificationT(applyTmachineTlearningTorTlexiconTapproachesTtoTclassifyTtheTtext).TTheTdetectionTofTironicTexpressionsTinTTripAdvisorTreviewsTisTanTopenTproblemTthatTcouldThelpTtoTextractTmoreTvaluableTinformation.TNeedTnewTapproachesTtoTfixTtheTpositive,TnegativeTandTneutralityTviaTconsensusTamongTSAMs. FarmanTAliTetTalT12,TMergedTontologyTandTSVMTbasedTrecommendationTandTinformationTextractionTsystedTautomatesTtheTextractionTofTpreciseTdataTfromTtheTInternetTandTsuggestsTaccurateTitemsTforTdisabledTusers.TATnumberTofTresonableTissuesTareTeffectivelyTconsidered.TOralTquestionsTconversionTintoTtheTrightTformatTforTaTkeywordTbasedTmostlyTcomputerTprogram.TItTcategorisesTtheTretrievedTinformationaTandTeffectivelyTcomputesTtheTtheTpolarityTforTtheTdesiredTitemsTthatTneedTtoTbeTrecommended. CHAPTERT3…

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