基于自然语言的Apriori关联规则的视觉挖掘方法

基于自然语言的Apriori关联规则的视觉挖掘方法摘要：抽象-可视化数据挖掘技术可以以图形方式向用户展示数据挖掘过程，从而使用户更易于理解挖掘过程及其结果，而且在数据挖掘中也非常重要。然而，现在大多数视觉数据挖掘都是通过可视化的结果而进行的。同时，它不适用于关联规则的可视化处理的图形显示。鉴于上述缺点，本文采用自然语言处理方法，以自然语言视觉地进行Apriori关联规则的整体挖掘过程，包括数据预处理，挖掘过程和挖掘结果的可视化显示为用户提供了一套具有更多感知和更易于理解的特征的集成方案关键字：apriori关联规则数据挖掘可视化1引言视觉数据挖掘技术是可视化技术和数据挖掘技术的结合。使用计算机图形、图像处理技术等方法将数据挖掘的源数据，中间结果和最终挖掘结果转换成易于理解的图形或图像，然后进行贯穿的理论，方法和技术交互式处理。根据数据挖掘应用中可视化的不同阶段，数据挖掘的可视化可以分为源数据可视化，挖掘过程可视化和结果可视化。源数据可视化源数据可视化方法在数据挖掘之前，以可视化的形式将整个数据集呈现给用户。目的是使用户能够快速找到有趣的地区，从而实现挖掘目标和目标的下一步。过程可视化过程可视化实现起来相当复杂。主要有两种方法-一种是在采矿过程中可视化地呈现中间结果，并使用户根据中间结果的反馈方便地调整参数和约束。另一种方法是以图标和流程图的形式保持整个数据挖掘过程，根据用户可以观察数据源，数据集成，清理和预处理过程以及采矿结果的存储和可视化等等。（3）结果可视化数据挖掘结果可视化是指在采矿过程结束时以图形和图像的形式描述挖掘结果或知识，以提高用户对结果的理解，并使用户更好地评估和利用采矿结果。2、国外家庭视觉数据挖掘研究状况目前，视觉数据挖掘技术的研究在国内外都处于起步阶段，如何使用可视化技术来显示利用各种数据挖掘算法生成后的模型。该方向的主要研究内容是通过一些特殊视觉图形中的关联规则、决策树和聚类等算法向用户显示生成的结果，以帮助用户更好地了解结果数据挖掘模型。典型的业务应用程序是IBMSPSSModeler，开源工具包括Weka、Orange、GGobi和KNIME，以及GoogleVisualPublicPlatform：PublicDataExplorer。视觉数据挖掘工具是一种很好的数据分析工具，在行业应用中，使用可视化数据挖掘工具显示数据挖掘更为明确，结合数据挖掘技术，更有利于分析的数据挖掘结果。目前，关联规则的可视化研究主要集中在可视化数据和关联规则结果上，而挖掘过程可视化存在很多缺陷。特别是在视觉演示过程中，基本采用图形形式。在实践中已经发现，图形方法不适合在过程中显示关联规则及其结果。因为对于关联规则，我们的目的是找到频繁的项目集，最好的结果显示它们是文本，同时对于最终获得的关联规则，图形应用程序不能够很好地显示，最好的方法是用基于自然语言的方式显示应用程序。本文提出了基于自然语言的Apriori关联规则的视觉挖掘方案。该方案的预处理，中间过程和采矿结果各个方面均可视化。旨在通过最可接受的自然语言作为工具，实现整个采矿过程的视觉演示。3基于APRIORI协会规则的可视化采矿的基本理念本文提出的关联规则的视觉挖掘基本思想是在数据挖掘的整个过程中，提前提出关联规则的视觉挖掘基本上是关于采矿结果可视化的，很少涉及中间和预处理过程中的可视化。对于结果可视化，图形方法是主要采用的显示方式，如使用平行坐标法，有向图法等。然而，对于关联规则，通过频繁项目集和关联规则的方式进行图形显示似乎无能为力。协会只是反映规则，规则最直接的形式是使用自然语言，而奥术公式和图形对于那些非常专业的人员而言是可以理解的，不适合普及。而且，当然，充分运用反映关联规则的自然语言对实现有一定困难。在本文中，采用自然语言的形式，以视觉方式展示了整个采矿过程。可视化过程如图1所示图1关联规则的视觉过程表1数学分数变换规则序号条件等级A1Math>=85优A2Math>=60andMath<=85中A3Math<60差（1）数据预处理数据预处理是整个数据挖掘的关键，也是第一步，一般程序自动完成工作并显示差异。本文采用完全互动的预处理操作可视化方法，首先构建用户定义的自然语言转换规则库，易于编辑规则，其最终目标是将属性值转换为自然语言。例如，表1可以被定义为这样的规则，根据得分值，不同的分数可以被转换成不同的代码。（2）采矿过程挖掘过程的可视化主要体现在中间挖掘结果的视觉显示和用户与系统之间的相互作用。对于关联规则，中间挖掘结果体现在频繁项集合的显示中，以供用户观察采矿过程正确或不正确，同时根据交互程序，用户可以及时地介入方案进行运作（3）采矿结果挖掘结果可视化主要是基于最大频繁项集来提取关联规则，并通过转换规则将编码关联规则转换为自然语言形式。用户可以一目了然地了解规则的含义。4APRIORI协会规则的视觉采矿实施数据预处理可视化构建转换表：转换表（字段名称，代码，条件和含义）图2数据预处理可视化用户可以在转换规则表中进行编辑，包括添加，删除等。形成转换规则表后，从数据预处理开始。如图2所示，首先打开原始数据表，扫描表中的每个属性和值，并在转换规则表中查找属性和值，并进行转换，如果没有找到相应的属性和值，然后反复进行错误处理，直到转换完成B视觉挖掘过程1）采矿参数设定在挖掘之前，用户选择支持度和置信度，然后开始进行数据挖掘，其中可以随时观察采矿频繁项目集和最大频繁项集的变化，如果异常，可能会及时终止程序的运行并重新选择参数以重复数据挖掘。2）中间结果显示在采矿过程中，可以显示初始数据项集，频繁项集，最大频繁项集，以便观察用户数据挖掘的整个过程。C采矿结果可视化1）根据模糊关联理论建立关联规则的模糊运算符规则有两个限制，一个是支持度，另一个是置信度。建立关联规则的关键在于信心度量，因此本文以信度为参照。根据需要，在本文中，置信度取0-100的水平作为边界，所以模糊理论的领域表达为[0,100]。模糊集的特征函数被称为隶属函数，它是描述逐渐变化的东西和“中介转型”现象的关键。下属功能有很多种，常用的有三种形式：正常型，基于环型类型和环型。从经验来看，建议使用基于环型类型或环型的会员功能来描述模糊操作者，而选择正常类型来描述模糊运算符。运算符是其模糊度的描述，本文表明了关联规则的建立程度。我们使用“很可能”，“可能”，“更可能”，“可能”修改关联规则的建立程度。其中a为阈值，λ为操作者的对应值，Hλ为定量描述模糊值的操作符。设置A的模糊值，定义Hλ，对于HλA=Aλ，并且λ的值的相应语义含义应该是“很可能”，λ=4;“可能”，λ=2;“更可能”，Aλ=0.5;“可能”，λ=0.25。基于模糊算子，模糊条件由公式（1）得出，通过公式（1）可以推导出精确的范围：2）关联规则的自然语言转换如图3所示，为关联规则形成以符号形式显示，扫描转换表，扫描规则中的每个符号，将符号转换为自然语言，最后通过自然语言将符号中的显示规则转换为规则。例如，符号规则：B2-->F3转换成：中等职业成就-->方向（就业）表3挖掘结果的可视化过程为了测试本文方法的可行性，根据Apriori关联规则挖掘算法，编写了学生成绩与毕业指导关系的数据挖掘程序。以N大学X学院C学院为例：64名学生，5名综合表现属性，1名毕业方向属性，挖掘过程和结果如图4和图5所示。从图4和图5可以看出，它在整个过程中主要建立自然语言转换规则库，然后将属性值转换为代码，并使用代码进行数据挖掘。可以观察采矿过程中频繁项集的变化，使用户能够及时调整初始参数。挖掘结果可以直接以自然语言显示，以提高规则的可读性。图4原始数据的预处理图5数据挖掘结果5结论本文针对目前大多数现有的视觉数据挖掘技术已经集中在数据挖掘结果的可视化这两个缺陷，同时对于Apriori关联规则，其视觉处理不适合图形显示，提出了一种基于自然语言的视觉处理方法。该方法可以对关联规则的Apriori算法进行数据预处理，并对整个挖掘过程中的挖掘过程和结果进行自然语言视觉处理。它提供了一套具有更多视觉和易于理解的特征的集成方案。扩大了视觉数据挖掘过程的应用范围，有利于数据挖掘技术的推广应用。参考文献：[1]XieQinghua,ZhangNingrong,SongYishenetc,"TheVisualModelMethodandTechnologyofClusteringDataMining",JoumalofPLAuniversityofscienceandtechnology(NaturalScienceEdition),vo1.I6,no.I,pp.7-15,2015.[2]ZhangJun,"ResearehandImplementationofVisualDataMiningTechnology",JoumalofChongqingTechnologyandBusinessUniversity(NaturalScienceEdition),vo1.30,no.3,pp.58-61,95,2013.[3]WangJing,"TheResearchandApplicationofVisualTechnologyinDataMining",JilinUniversitypress,Changchun,2009.[4]HuJun,"TheVisualDataMiningModelandItsApplicationResearch",BeijingJiaotongUniversitypress,Beijing,2009.[5]SongChengzhang,HuangXiaodong,LiPeng,ete,"PublieSentimentLargeDataAnalysisBasedonProcessingandItsVisualizationStudy",FujianComputer,no.5,pp.19-21,2014.[6]LiHuijun,LiZhiquan,"TheResearchontheVisualClusteringMethodBasedonImprovingRadarMap",JournalofYanshanUniversity,vo1.5,no.1,pp.58-62,2013.[7]SunQiunian,RaoYuan,"TheResearchOverviewofNetworkDataVisualizationTechnologyBasedontheCOITelationAnalysis",ComputerSeience,no.6a,pp.484-488,2015.[8]LiYang,HaoZhifeng,XiaoYanShan,ete,"TheMultidimensionalDataVisualizationoftheDifferentialPrivacyDPEk-meansundertheDataAggregation",Small-sizeComputerandMicrocomputerSystem,no.7,pp.1637-1640,2013.[9]HuangBin,XuShuren,PuWei,"TheDesignandImplementationofDataMiningPlatformBasedonMapReduee",ComputerEngineeringandDesign,no.2,pp.495-50I,2013.[10]QiSenyu,DuJinglin,QianShenshen,etc,"TheResearchOverviewofMultidimensionalDataVisualizationTechnology[J].SoftwareGuide2015,14(7):15to17.[11]YangZhenyu,WangXiaoyue,BaiRujiang,"TheComparativeAnalysisResearchonMajorForeignVisualDataMiningOpen­sourceSoftwar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LiYanCollegeo/ComputerScienceandTechnologyInnerMongoliaUniversityForNationalities,tongliao028043,CHINAliyan_yx@126.comaccordingtothefeedbackoftheintermediateresults.Anotherwayistokeepthewholedataminingprocessintheformoficonsandflowchartsandaccordingtothem,theusercanobservethedatasource,thedataintegration,thecleaningandpretreatmentprocess,andthestorageandvisualizationofminingresults,etc.3)TheresultvisualizationThedata-miningresultvisualizationreferstodescribingtheminingresultsorknowledgeintheformofgraphicsandimagesattheendoftheminingprocessinordertoimprovetheuser'sunderstandingoftheresults,andmaketheuserbetterevaluateandmakeuseoftheminingresults.VISUALDATAMININGRESEARCHSTATUSATHOMEANDABROADAtpresent,thestudyofvisualdataminingtechnologyisintheascendantbothathomeandabroad,ofwhichhowtousethevisualizationtechnologytodisplayconsequencemodelsgeneratedwithvariouskindsofdataminingalgorithm.Themainresearchcontentofthisdirectionistodisplaythegeneratedresultstotheuserwithalgorithmsuchasassociationrules,decisiontreeandclusteringinsomespecialvisualgraphicinordertohelptheuserunderstandtheresultdataminingmodelbetter.ThetypicalbusinessapplicationisIBMSPSSModeler,andtheopensourcetoolsareWeka,Orange,GGobiandKNIME,plusGoogleVisualPublicPlatform:PublicDataExplorer.Visualdataminingtoolisagoodkindofdataanalysistoolandinindustryapplication,itismoreexplicittoshowthedataminingwiththeuseofvisualdataminingtool,combinedwiththedataminingtechnologyandalsoitismoreconducivetotheanalysisofthedataminingresults.Atpresentthevisualizationstudyoftheassociationrulearemainlyconcentratedonthevisualizationofdataandtheassociationruleresults[8-16],whiletherearealotofdefectsontheminingprocessvisualization.Especiallyintheprocessofvisualdemonstration,thegraphicformisbasicallyadopted.Ithasbeenfoundinpracticethatthegraphicmethodisnotsuitableforthedisplayoftheassociationruleinthemiddleoftheprocessanditsresults.Becauseasfortheassociationrule,ouraimistofindfrequentitemsets,andthebestresultdisplaythemistext,meanwhileasforthefinally-gainedassociationrule,thegraphicapplication572displayispaleandthebestmethodisthedisplayofapplicationbasedonnaturallanguage.ThispaperproposesavisualminingschemeofAprioriassociationrulebasedonnaturallanguage.Thepretreatment,theintermediateprocess,andminingresultsintheschemearevisualizedinalldimensions.Itaimstorealizethevisualdemonstrationofthewholeminingprocesswiththeapplicationofthemostacceptablenaturallanguageasatool.BASICIDEASOFVISUALMININGBASEDONAPRIORlASSOCIATIONRULEThevisualminingbasicideaontheassociationruleputforwardinthispaperisthroughouttheentireprocessofdatamining.Thevisualminingontheassociationruleputforwardbeforehandwerebasicallyaboutthemining-resultvisualization,verylittlereferringtothevisualizationintheintermediateandpretreatmentprocess.Asfortheresultvisualization,thegraphicmethodisamostlyadoptedwaytodisplay,suchasusingthemethodofparallelcoordinates,thedirectedgraphmethodandetc.Fortheassociationrule,however,graphicaldisplaybythewayoffrequentitemsetsandassociationrulesseemspowerlesswithoutvisualization.Associationrulejustreflectsrulesandforrulesthemostdirectformistousenaturallanguage,whilearcaneformulaeandgraphiccanbeonlyunderstandableforthoseveryprofessionalpersonnelandnotsuitableforpopularization.And,ofcourse,fullyapplicationofnaturallanguagereflectingassociationruleontheimplementationhasthecertaindifficulty.Inthispaper,theformofnaturallanguageisadoptedtodisplaythewholeprocessofminingvisually.ThevisualizationprocessisasshowninFigure1.FigureI.ThevisualprocessofassociationruleTABLE1.MATHEMATICALSCORESTRANSFORMINGRULECodePrerequisiteImplicationAlMath>�85GoodinmathA2Math>�60andMath<�85MediuminmathA3Math<60Poorinmath

1)DatapreprocessingDatapreprocessingisthekeytothewholedatamining,alsothefirststep,andthegeneralprocedureisautomaticallytofinishtheworkandvisualizedifferences.Thispaperadoptsafullyinteractivevisualizationmethodforpretreatmentoperation,andfirsttobuildnaturallanguagetransformationrulesbase,whichisdefinedbytheuser,iseasyforeditingrules,anditsultimategoalistoconverttheattributevalueintonaturallanguage.Forinstance,TableIcanbedefinedassuchrulesthataccordingtoscorevaluesdifferentscorescanbeconvertedintodifferentcodes.2)TheminingprocessThevisualizationofminingprocessofismainlyembodiedinthevisualdisplayoftheintermediateminingresultsandtheinteractionbetweentheuserandthesystem.Forassociationrules,theintermediateminingresultsareembodiedinthedisplayoffrequentitemsetsinordertoservefortheusertoobservetheminingprocesscorrectorincorrect,andatthesametime,accordingtotheinteractiveprogram,theusercanintervenetheprogramoperationtimely.3)TheminingresultTheminingresultvisualizationismainlybasedonmaximumfrequentitemsetstoextractassociationrulesandconvertcodingassociationrulesintonaturallanguageformthroughtransformationrules.Theusercanbeclearataglancetounderstandthemeaningofrules.IV. THEVISUALMININGIMPLEMENTATIONOFAPRIORIASSOCIATIONRULEDatapreprocessingvisualizationBuildthetransitiontable:Thetransitiontable(fieldname,code,conditionandimplication)OutsetNoNo ErrorFigure2.Datapreprocessingvisualization573Theusercaneditonthetransformationruletable,includingadding,deleting,andsoon.Afterformingthetransformationruletable,itistostartwithdatapreprocessing.AsshowninFigure2,firstlyopentheoriginaldatatable,scanroundeachattributeandvaluesinthetable,andlookuptheattributeandvalueinthetransformingruletableandconvert,ifthereisnocheckinthecorrespondingattributesandvalues,thenproceedtheerrorhandlingrepeatedlyuntiltheconversionisfinished.Visualminingprocess1)MiningparameterssettingBeforeexcavation,supportdegreeandconfidencedegreeareselectedbytheuser,andthenbegintodatamining,duringwhichitmayatanytimeobserveminingfrequentitemsetsandthechangeofthemaximumfrequentitemsets,ifabnormal,itmayterminatetheprogramrunninginatimelymannerandreselectparameterstorepeatdatamining.2)TheintermediateresultdisplayDuringtheprocessofmining,itcandisplaytheinitialdataitemsets,frequentitemsets,themaximumfrequentitemsetsinordertoobservethewholeprocessofdataminingfortheuser.Theminingresultvisualization1)ThefuzzymoodoperatorofassociationrulesEstablishedaccordingtothetheoryoffuzzyassociationrule,therearetwoconstraints,oneisthesupportdegreeandtheotheristhedegreeofconfidence.Thekeytothefoundingofassociationruleistheconfidencedegree,thusthispaperadoptsconfidencedegreeasaconditionofitsoperator.Accordingtotheneed,inthispaper,theconfidencedegreetakeslevelsof0-100astheboundary,sothedomainofthefuzzytheoryisexpressedas[0,100].Thecharacteristicfunctionofthefuzzysetisknownasthemembershipfunctionanditisthekeytodescribegradualchangingthingsandthephenomenonof"theintermediarytransition".Thesubordinatefunctionhasmanysortsandtherearethreekindsofformswhicharefrequentlyused:normaltype,ontheringtypeandringtype.Fromserviceexperience,itisadvisabletousemembershipfunctionsofontheringtypeorundertheringtypetodescribethefuzzymoodoperatorwhileitismoreappropriatetochoosethenormaltypetodescribethebluroperator.Themoodoperatoristhedescriptionofitsfuzzydegree,andinthispaperitindicatestheestablishmentdegreeofassociationrule.Weuse"verylikely","probably","morelikely","likely"tomodifytheestablishmentdegreeofassociationrule.Thispaperusesthefollowingform:{lX:2:Y[1+(x:Y)2r'Ji(X)XCj)<Y

Amongthem,isasthresholdvalue,Aisasthecorrespondingvalueofthemoodoperator,andHAisasthemoodoperatortoquantitativelydescribethefuzzyvalue.SetupthefuzzyvalueforA,defineHI-,forHAA=AA,andthecorrespondingsemanticimplicationofthevalueofI-,shouldbe"verylikely",I-,=4;"Probably",A=2;"Morelikely",A=0.5;"Likely",I-,=0.25.Basedonfuzzymoodoperator,thefuzzyconditionisderivedfromtheformula(1),bytheformula(1)itcandeducethepreciserangefor:1/5t1/5t11(2)2)ThenaturallanguageconversionofassociationruleAsshowninFigure3,fortheassociationruleformedbythedisplayinsymbolicform,scanconversiontable,scaneachsymbolintherule,convertsymbolsintonaturallanguage,andfinallyusetherulebythedisplayinsymbolstoconvertintotherulebythedisplayinnaturallanguage.Forinstance,symbolrule:B2--+F3convertinto:mediumprofessionalachievementin--+direction(employment)ScanningtheassociationruleQueryfortherulebaseTransformingintonaturallanguagNoFigure3.ThevisualizationprocessofminingresultTHEINSTANCESTotestthefeasibilityofthemethodinthispaper,writeadataminingprocedureoftherelationbetweenstudents'achievementsandgraduationdirectionsbasedonthealgorithmofAprioriassociationrulemining.TakeXHanClassofCCollegeinNuniversityasanexample:64students,5comprehensiveperformanceattributes,1graduationdirectionattribute,theminingprocessandresultareshowninFigure4andFigure5.AsyoucanseefromFigure4andFigure5,itinthewholeprocessprimarilyestablishthenaturallanguagetransformationrulebase,thenconvertattributevaluesintocodes,andusecodetoconductdatamining.Itcanobservechangesoffrequentitemsetsintheminingprocessandmakesiteasyfortheusertotimelyadjustinitialparameters.Theminingresultscanbedirectlydisplayedinnaturallanguagetoimprovethereadabilityoftherules.574VI. THECONCLUSIONThispaper,inviewofsuchtwodefectsthatmostpresentvisualdataminingtechnologieshavefocusedonthevisualizationofdataminingresults,atthesametime,forAprioriassociationruleitsvisualprocessingisnotsuitableforgraphicaldisplay,proposesavisualprocessingmethodbasedonnaturallanguage.ThismethodcandodatapreprocessingontheApriorialgorithmofassociationrule,anddoanaturallanguagevisualhandlingontheminingprocessandresultsduringthewholeminingprocess.Itprovidesanintegratesetofschemewhichhasthecharacteristicsofmorebeingvisualandeasytounderstand.Itexpandstheapplicationrangeofthevisualdataminingprocessandisconduciveforthepromotionandapplicationofdataminingtechnology.Figure4.ThepreprocessingoftheoriginaldataFigure5.Thedataminingresult

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