MATLAB下的SuperpixelSegmentationusingLinearSpectralClustering实现代码，直接可用。
附赠LSC和supp两篇论文

ZigbeeClusterLibraryRevision7，第7版，zigbee协会最新版ZCL文档

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

GRC(Graph-basedRelaxedClustering)是一种具有便捷性和自适应性的谱聚类算法,但对于大数据集,繁重的时间开销限制了其实用性.针对此不足,该文通过对GRC聚类指示向量进行约束并融合中心约束型最小包含球(Center-ConstrainedMinimalEnclosingBall,CCMEB)理论提出了大数据集快速谱聚类算法CCMEB-CGRC.该算法继承GRC的便捷性和自适应性的同时又具有渐近线性时间复杂度的优点,从而较好地解决了大数据集快速有效谱聚类的问题.仿真实验的结果验证了该算法的有效性和快速性.

hadoopk-means算法实现java工程的打包类，可直接在terminal中运行，运行命令为：$HADOOP_HOME/bin/hadoopjarClusterDemo.jarmain.Cluster然后直接确定就可以看到提示的运行参数或者参考下面：+"\n" +"\t:输入文件路径\n" +"\t:输出文件路径\n" +"\t:初始中心路径\n" +"\t:临时输出文件路径\n" +"\t:循环最大次数\n" +"\t:聚类中心变化阈值\n" +"\t:聚类中心数目\n" +"\t:原始数据属性数目\n" +"\t:reduce数目");

本资料面向LIN总线初学者，对什么是LIN，LIN的特征，物理层、协议层及应用层相关规定进行说明。
本资料主要是针对LIN2.1讲解。
使用注意事项.............................................................................................................................................11.LIN是什么?.........................................................................................................................................41.1LIN子网(Cluster)与节点(Node)............................................................................................................51.2主/从机节点与主/从机任务..................................................................................................................72.LIN的特点.............................................................................................................................................83.LIN协议层.............................................................................................................................................93.1帧的结构.............................................................................................................................................93.1.1同步间隔段(BreakField)...................................................................................................................93.1.2同步段(SyncByteField)..................................................................................................................103.1.3受保护ID段(ProtectedIdentifierField)..............................................................................................113.1.4数据段(DataField)...........................................................................................................................123.1.5校验和段(ChecksumField)..............................................................................................................13

MySQL8.0ReferenceManualIncludingMySQLNDBCluster8.0

Over110effectiverecipestohelpyoubuildandoperateOpenStackcloudcomputing,storage,networking,andautomationAboutThisBookExploremanynewfeaturesofOpenStack'sJunoandKiloreleasesInstall,configure,andadministercoreprojectswiththehelpofOpenStackObjectStorage,BlockStorage,andNeutronNetworkingservicesHarnesstheabilitiesofexperiencedOpenStackadministratorsandarchitects,andrunyourownprivatecloudsuccessfullyPractical,real-worldexamplesofeachserviceandanaccompanyingVagrantenvironmentthathelpsyoulearnquicklyInDetailOpenStackOpenSourcesoftwareisoneofthemostusedcloudinfrastructurestosupportsoftwaredevelopmentandbigdataanalysis.Itisdevelopedbyathrivingcommunityofindividualdevelopersfromaroundtheglobeandbackedbymostoftheleadingplayersinthecloudspacetoday.Itissimpletoimplement,massivelyscalable,andcanstorealargepoolofdataandnetworkingresources.OpenStackhasastrongecosystemthathelpsyouprovisionyourcloudstorageneeds.AddOpenStack'senterprisefeaturestoreducethecostofyourbusiness.Thisbookwillshowyouthestepstobuildupaprivatecloudenvironment.Atthebeginning,you'lldiscovertheusesofcloudservicessuchastheidentityservice,imageservice,andcomputeservice.You'lldiveintoNeutron,theOpenStackNetworkingservice,andgetyourhandsdirtywithconfiguringML2,networks,routers,andDistributedVirtualRouters.You'llthengathermoreexpertknowledgeonOpenStackcloudcomputingbymanagingyourcloud'ssecurityandmigration.Afterthat,wedelveintoOpenStackObjectstorageandhowtomanageserversandworkwithobjects,cluster,andstoragefunctionalities.Also,asyougodeeperintotherealmofOpenStack,you'lllearnpracticalexamplesofBlockstorage,LBaaS,andFWaaS:installationandconfigurationcoveredgroundup.Finally,youwilllearnOpenStackdashboard,AnsibleandForeman,Key

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

PragmaticAI:AnIntroductiontoCloud-BasedMachineLearningBy作者:NoahGiftISBN-10书号:0134863860ISBN-13书号:9780134863863Edition版本:1出版日期:2018-08-19pages页数:256ContentsPrefaceAcknowledgmentsAbouttheAuthor1:IntroductiontoPragmaticAl1IntroductiontoPragmaticAl2AlandMLToolchain3SpartanAlLifecyclell:AlintheCloud4CloudAlDevelopmentwithGoogleCloudPlatform5CloudAlDevelopmentwithAmazonWebServiceslⅢ:CreatingPracticalAlApplicationsfromScratch6PredictingSocial-MediaInfluenceintheNBA7CreatinganIntelligentSlackbotonAWS8FindingProjectManagementInsightsfromaGitHubOrganization9DynamicallyOptimizingEC2InstancesonAWS10RealEstate11ProductionAlforUser-GeneratedContentAAlAcceleratorsBDecidingonClusterSizeIndex

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。