数据挖掘概念与技术（Dataminingconceptsandtechniques3rd）中文版，数据挖掘经典书籍，最新第三版的中文书籍，清晰,好用的资源,描述了经典算法，适用于专业研究人员。

DataMiningwithRLearningwithCaseStudies英文无水印原版pdfpdf所有页面使用FoxitReader、PDF-XChangeViewer、SumatraPDF和Firefox测试都可以打开本资源转载自网络，如有侵权，请联系上传者或csdn删除查看此书详细信息请在美国亚马逊官网搜索此书

DatabaseSystemConcepts——数据库系统概念第六版（英文版）作者：AbrahamSilberschatz(YaleUniversity)HenryF.Korth(LehighUniversity)S.Sudarshan(IndianInstituteofTechnology,Bombay)本书目录：Chapter1Introduction1.1Database-SystemApplications11.2PurposeofDatabaseSystems31.3ViewofData61.4DatabaseLanguages91.5RelationalDatabases121.6DatabaseDesign151.7DataStorageandQuerying201.8TransactionManagement221.9DatabaseArchitecture231.10DataMiningandInformationRetrieval251.11SpecialtyDatabases261.12DatabaseUsersandAdministrators271.13HistoryofDatabaseSystems291.14Summary31Exercises33BibliographicalNotes35Chapter2IntroductiontotheRelationalModel2.1StructureofRelationalDatabases392.2DatabaseSchema422.3Keys452.4SchemaDiagrams462.5RelationalQueryLanguages472.6RelationalOperations482.7Summary52Exercises53BibliographicalNotes55Chapter3IntroductiontoSQL3.1OverviewoftheSQLQueryLanguage573.2SQLDataDefinition583.3BasicStructureofSQLQueries633.4AdditionalBasicOperations743.5SetOperations793.6NullValues833.7AggregateFunctions843.8NestedSubqueries903.9ModificationoftheDatabase983.10Summary104Exercises105BibliographicalNotes112Chapter4IntermediateSQL4.1JoinExpressions1134.2Views1204.3Transactions1274.4IntegrityConstraints1284.5SQLDataTypesandSchemas1364.6Authorization1434.7Summary150Exercises152BibliographicalNotes156Chapter5AdvancedSQL5.1AccessingSQLFromaProgrammingLanguage1575.2FunctionsandProcedures1735.3Triggers1805.4RecursiveQueries1875.5AdvancedAggregationFeatures1925.6OLAP1975.7Summary209Exercises211BibliographicalNotes216Chapter6FormalRelationalQueryLanguages6.1TheRelationalAlgebra2176.2TheTupleRelationalCalculus2396.3TheDomainRelationalCalculus2456.4Summary248Exercises249BibliographicalNotes254Chapter7Datab

支持向量机源码，可在www.csie.ntu.edu.tw/~cjlin/libsvm/下载到最新版本，该版本是2013年4月更新的，3.17版。
压缩包里面有源代码和文档。
以下摘自前述网站：IntroductionLIBSVMisanintegratedsoftwareforsupportvectorclassification,(C-SVC,nu-SVC),regression(epsilon-SVR,nu-SVR)anddistributionestimation(one-classSVM).Itsupportsmulti-classclassification.Sinceversion2.8,itimplementsanSMO-typealgorithmproposedinthispaper:R.-E.Fan,P.-H.Chen,andC.-J.Lin.WorkingsetselectionusingsecondorderinformationfortrainingSVM.JournalofMachineLearningResearch6,1889-1918,2005.Youcanalsofindapseudocodethere.(howtociteLIBSVM)OurgoalistohelpusersfromotherfieldstoeasilyuseSVMasatool.LIBSVMprovidesasimpleinterfacewhereuserscaneasilylinkitwiththeirownprograms.MainfeaturesofLIBSVMincludeDifferentSVMformulationsEfficientmulti-classclassificationCrossvalidationformodelselectionProbabilityestimatesVariouskernels(includingprecomputedkernelmatrix)WeightedSVMforunbalanceddataBothC++andJavasourcesGUIdemonstratingSVMclassificationandregressionPython,R,MATLAB,Perl,Ruby,Weka,CommonLISP,CLISP,Haskell,OCaml,LabVIEW,andPHPinterfaces.C#.NETcodeandCUDAextensionisavailable.It'salsoincludedinsomedataminingenvironments:RapidMiner,PCP,andLIONsolver.Automaticmodelselectionwhichcangeneratecontourofcrossvaliationaccuracy.

DavidJ.HandDepartmentofMathematics,ImperialCollegeLondon,London,UKDataminingisthediscoveryofinteresting,unexpectedorvaluablestructuresinlargedatasets.Assuch,ithastworatherdifferentaspects.Oneoftheseconcernslarge-scale,‘global’structures,andtheaimistomodeltheshapes,orfeaturesoftheshapes,ofdistributions.

DataMiningandAnalysis_FundamentalConceptsandAlgorithms_2014

DataMining:TheTextbookBy作者:CharuC.AggarwalISBN-10书号:3319141414ISBN-13书号:9783319141411Edition版本:2015出版日期:2015-04-14pages页数:(734)$89.99Thistextbookexploresthedifferentaspectsofdataminingfromthefundamentalstothecomplexdatatypesandtheirapplications,capturingthewidediversityofproblemdomainsfordataminingissues.Itgoesbeyondthetraditionalfocusondataminingproblemstointroduceadvanceddatatypessuchastext,timeseries,discretesequences,spatialdata,graphdata,andsocialnetworks.Untilnow,nosinglebookhasaddressedallthesetopicsinacomprehensiveandintegratedway.Thechaptersofthisbookfallintooneofthreecategories:Fundamentalchapters:Datamininghasfourmainproblems,whichcorrespondtoclustering,classification,associationpatternmining,andoutlieranalysis.Thesechapterscomprehensivelydiscussawidevarietyofmethodsfortheseproblems.Domainchapters:Thesechaptersdiscussthespecificmethodsusedfordifferentdomainsofdatasuchastextdata,time-seriesdata,sequencedata,graphdata,andspatialdata.Applicationchapters:Thesechaptersstudyimportantapplicationssuchasstreammining,Webmining,ranking,recommendations,socialnetworks,andprivacypreservation.Thedomainchaptersalsohaveanappliedflavor.Appropriateforbothintroductoryandadvanceddataminingcourses,DataMining:TheTextbookbalancesmathematicaldetailsandintuition.Itcontainsthenecessarymathematicaldetailsforprofessorsandresearchers,butitispresentedinasimpleandintuitivestyletoimproveaccessibilityforstudentsandindustrialpractitioners(includingthosewithalimitedmathematicalbackground).Numerousillustrations,examples,andexercisesareincluded,withanemphasisonsemanticallyinterpretableexamples.

nmanydataanalysistasks,oneisoftenconfrontedwithveryhighdimensionaldata.Featureselectiontechniquesaredesignedtofindtherelevantfeaturesubsetoftheoriginalfeatureswhichcanfacilitateclustering,classificationandretrieval.Thefeatureselectionproblemisessentiallyacombinatorialoptimizationproblemwhichiscomputationallyexpensive.Traditionalfeatureselectionmethodsaddressthisissuebyselectingthetoprankedfeaturesbasedoncertainscorescomputedindependentlyforeachfeature.Theseapproachesneglectthepossiblecorrelationbetweendifferentfeaturesandthuscannotproduceanoptimalfeaturesubset.InspiredfromtherecentdevelopmentsonmanifoldlearningandL1-regularizedmodelsforsubsetselection,weproposehereanewapproach,called{\emMulti-Cluster/ClassFeatureSelection}(MCFS),forfeatureselection.Specifically,weselectthosefeaturessuchthatthemulti-cluster/classstructureofthedatacanbebestpreserved.Thecorrespondingoptimizationproblemcanbeefficientlysolvedsinceitonlyinvolvesasparseeigen-problemandaL1-regularizedleastsquaresproblem.ItisimportanttonotethatMCFScanbeappliedinsuperised,unsupervisedandsemi-supervisedcases.Ifyoufindthesealgoirthmsuseful,weappreciateitverymuchifyoucanciteourfollowingworks:PapersDengCai,ChiyuanZhang,XiaofeiHe,"UnsupervisedFeatureSelectionforMulti-clusterData",16thACMSIGKDDConferenceonKnowledgeDiscoveryandDataMining(KDD'10),July2010.BibtexsourceXiaofeiHe,DengCai,andParthaNiyogi,"LaplacianScoreforFeatureSelection",AdvancesinNeuralInformationProcessingSystems18(NIPS'05),Vancouver,Canada,2005Bibtexsource

RapidMiner最经典的三部教程（1）PredictiveAnalyticsandDataMining，由BalaDeshpande博士所著（2）RapidMinerDataMiningUseCasesandBusinessAnalyticsApplications，非常要的图书，在亚马逊书店上卖的非常好（3）RapidMiner-v6-user-manual，用户手册，非常全

*Packedwithmorethanfortypercentnewandupdatedmaterial,thiseditionshowsbusinessmanagers,marketinganalysts,anddataminingspecialistshowtoharnessfundamentaldataminingmethodsandtechniquestosolvecommontypesofbusinessproblems　　*Eachchaptercoversanewdataminingtechnique,andthenshowsreadershowtoapplythetechniqueforimprovedmarketing,sales,andcustomersupport　　*Theauthorsbuildontheirreputationforconcise,clear,andpracticalexplanationsofcomplexconcepts,makingthisbooktheperfectintroductiontodatamining　　*Moreadvancedchapterscoversuchtopicsashowtopreparedataforanalysisandhowtocreatethenecessaryinfrastructurefordatamining　　*Coverscoredataminingtechniques,includingdecisiontrees,neuralnetworks,collaborativefiltering,associationrules,linkanalysis,clustering,andsurvivalanalysis

在日常工作中，钉钉打卡成了我生活中不可或缺的一部分。然而，有时候这个看似简单的任务却给我带来了不少烦恼。 每天早晚，我总是得牢记打开钉钉应用，点击"工作台"，再找到"考勤打卡"进行签到。有时候因为工作忙碌，会忘记打卡，导致考勤异常，影响当月的工作评价。而且，由于我使用的是苹果手机，有时候系统更新后，钉钉的某些功能会出现异常，使得打卡变得更加麻烦。 另外，我的家人使用的是安卓手机，他们也经常抱怨钉钉打卡的繁琐。尤其是对于那些不太熟悉手机操作的长辈来说，每次打卡都是一次挑战。他们总是担心自己会操作失误，导致打卡失败。 为了解决这些烦恼，我开始思考是否可以通过编写一个全自动化脚本来实现钉钉打卡。经过一段时间的摸索和学习，我终于成功编写出了一个适用于苹果和安卓系统的钉钉打卡脚本。