详细讲述了K-均值50年的发展历程，及现在用户的困境和发展趋势。

聚类分析不要忘记在pom.xml中添加“json-simple”作为项目的依赖在运行程序之前，将开源物理库导入项目（）要创建导入文件，请调用randomDataGenerator.js脚本var1是导入文件的标题（例如dataFile）var2是集群节点的数量（例如25）var3是概率（例如0.3）如果运行初始化而不导入数据文件，它将在变量中生成具有指定计数和边沿概率的随机点个人实验结果我已经从项目中创建了数据文件，节点是方法，边缘是函数调用。
初始状态描述在./SampleData/Analysis_Functions.txt文件中，固定的数据文件是./SampleData/analysis.json这是集群的初始状态这是形成的团簇./SampleData/Analysis_Cluster.txt文件中的文本形式也描述了群集

jar、pom文件citrusdubbodubbo-admindubbo-clusterdubbo-commondubbo-configdubbo-config-apidubbo-config-springdubbo-containerdubbo-container-apidubbo-container-javaconfigdubbo-container-jettydubbo-container-log4jdubbo-container-logbackdubbo-container-springdubbo-demodubbo-demo-apidubbo-demo-consumerdubbo-demo-providerdubbo-filterdubbo-filter-cachedubbo-filter-validationdubbo-monitordubbo-monitor-apidubbo-monitor-defaultdubbo-monitor-simpledubbo-parentdubbo-registrydubbo-registry-apidubbo-registry-defaultdubbo-registry-multicastdubbo-registry-redisdubbo-registry-simpledubbo-registry-zookeeperdubbo-remotingdubbo-remoting-apidubbo-remoting-grizzlydubbo-remoting-httpdubbo-remoting-minadubbo-remoting-nettydubbo-remoting-p2pdubbo-remoting-zookeeperdubbo-rpcdubbo-rpc-apidubbo-rpc-defaultdubbo-rpc-hessiandubbo-rpc-httpdubbo-rpc-injvmdubbo-rpc-memcacheddubbo-rpc-redisdubbo-rpc-restdubbo-rpc-rmidubbo-rpc-thriftdubbo-rpc-webservicedubbo-simpledubbo-testdubbo-test-benchmarkdubbo-test-benchmark-apidubbo-test-benchmark-clientdubbo-test-benchmark-serverdubbo-test-examplesfastjsonhessian-liteopensesame

zigbee3.0协议ZigBeeClusterLibrarySpecification

Sensu-Plugins-elasticsearch千寿资产从该存储库打包的Sensu资产是针对Sensuruby​​运行时环境构建的。
将这些资产用作SensuGo资源（检查，更改程序或处理程序）的一部分时，请确保在资源所需的资产列表中包括相应的Sensuruby​​运行时资产。
目前Ruby运行的资产，可以发现的。
功能性档案/bin/check-es-circuit-breakers.rb/bin/check-es-cluster-health.rb/bin/check-es-cluster-status.rb/bin/check-es-file-descri

英文原版，数字版，非影印版，无水印，有目录，第三版。
HowcanyoubringoutMySQL’sfullpower?WithHighPerformanceMySQL,you’lllearnadvancedtechniquesforeverythingfromdesigningschemas,indexes,andqueriestotuningyourMySQLserver,operatingsystem,andhardwaretotheirfullestpotential.Thisguidealsoteachesyousafeandpracticalwaystoscaleapplicationsthroughreplication,loadbalancing,highavailability,andfailover.UpdatedtoreflectrecentadvancesinMySQLandInnoDBperformance,features,andtools,thisthirdeditionnotonlyoffersspecificexamplesofhowMySQLworks,italsoteachesyouwhythissystemworksasitdoes,withillustrativestoriesandcasestudiesthatdemonstrateMySQL’sprinciplesinaction.Withthisbook,you’lllearnhowtothinkinMySQL.LearntheeffectsofnewfeaturesinMySQL5.5,includingstoredprocedures,partitioneddatabases,triggers,andviewsImplementimprovementsinreplication,highavailability,andclusteringAchievehighperformancewhenrunningMySQLinthecloudOptimizeadvancedqueryingfeatures,suchasfull-textsearchesTakeadvantageofmodernmulti-coreCPUsandsolid-statedisksExplorebackupandrecoverystrategies—includingnewtoolsforhotonlinebackups

Badclimatechangemaygreatlyincreasethefragilityofthecountry.Howtoevaluatetheimpactofclimatechangeandmitigatetheimpactofclimatechangehasbecomeanurgentproblem.Withregardtotaskone,adataenvelopmentanalysis(DEA)modelisestablishedtogetthecountry'sfragility.Firstofall,weselected4climatefactorsasinputindicatorsand5outputindicators.Then,weusetheentropymethodtodeterminetheweightandthenthenationalvulnerabilityisdivided.Atthesametime,wegettheconclusionthattemperatureaffectsGDPandthetimesofarmedconflictdirectlyandaffectsthefragilityindirectly.Inviewoftasktwo,wechooseSomaliaasanobjectofstudy.First,alltheindexesaredividedinto5levelsbythemethodofclusteranalysis.Second,weselect10countriesincludingSomalia,tosolvethedecisionunitmatrix.Then,usingthemodeloftheproblemone,itisfoundthattheincreaseintemperatureandrainfallwillcausethenationalvulnerabilitytoriseanddecrease,respectively.Finally,weassign4climateindicatorsto0ofthedecisionunits,anddrawtheconclusionthatnationalvulnerabilitywillbereducedwithouttheimpactofclimatefactors.Whenitcomestotaskthree,weusetheroughsettheorytoreducetheoutputindextothenumberofarmedconflicts.Then,weusetheBPneuralnetworkmodeltopredicttheconclusion:Thereisasignificantincreaseinfragilityincasesofmuchmorearmedconflictandabnormaltemperature.Whentheaverageannualarmedconflictiscertain,thenationalvulnerabilityindexwillfaceanincreasingturningpointatthetemperatureof10.01andtherainfallof1823mm.Astotaskfour,threepoliciesonenergyreductionandemissionreductionissuedbythegovernmenthavebeenselected,andamodelofcarboncycleisestablished.TakingChinaasanexample,wecalculatetheextentofthechangeoftheaveragetemperaturebyreducingthecarbondioxideemiss

基于FuzzyMeansCluster算法的高光谱遥感图像聚类（matlab实现）

文本聚类算法二次开发

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.

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。