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中文名: Web数据挖掘：挖掘Web内容模式、结构和用途作者: Zdravko MarkovDaniel T. Larose图书分类: 网络资源格式: PDF版本: 文字版出版社: Wiley Blackwell书号: 0471666556发行时间: 2007年04月01日地区: 美国语言: 英文简介: 内容介绍：This book introduces the reader to methods of data mining on the web, including uncovering patterns in web content (classification, clustering, language processing), structure (graphs, hubs, metrics), and usage (modeling, sequence analysis, performance).内容截图： 目录: PREFACEPART I: WEB STRUCTURE MINING1 INFORMATION RETRIEVAL AND WEB SEARCHWeb ChallengesWeb Search EnginesTopic DirectoriesSemantic WebCrawling the WebWeb BasicsWeb CrawlersIndexing and Keyword SearchDocument RepresentationImplementation ConsiderationsRelevance RankingAdvanced Text SearchUsing the HTML Structure in Keyword SearchEvaluating Search QualitySimilarity SearchCosine SimilarityJaccard SimilarityDocument ResemblanceReferencesExercises2 HYPERLINK-BASED RANKINGIntroductionSocial Networks AnalysisPageRankAuthorities and HubsLink-Based Similarity SearchEnhanced Techniques for Page RankingReferencesExercisesPART II: WEB CONTENT MINING3 CLUSTERINGIntroductionHierarchical Agglomerative Clusteringk-Means ClusteringProbabilty-Based ClusteringFinite Mixture ProblemClassification ProblemClustering ProblemCollaborative Filtering (Recommender Systems)ReferencesExercises4 EVALUATING CLUSTERINGApproaches to Evaluating ClusteringSimilarity-Based Criterion FunctionsProbabilistic Criterion FunctionsMDL-Based Model and Feature Evaluation.Minimum Description Length Principle.MDL-Based Model EvaluationFeature SelectionClasses-to-Clusters EvaluationPrecision, Recall, and F-MeasureEntropyReferencesExercises5 CLASSIFICATIONGeneral Setting and Evaluation TechniquesNearest-Neighbor AlgorithmFeature SelectionNaive Bayes AlgorithmNumerical ApproachesRelational LearningReferencesExercisesPART III: WEB USAGE MINING6 INTRODUCTION TO WEB USAGE MININGDefinition of Web Usage MiningCross-Industry Standard Process for Data MiningClickstream AnalysisWeb Server Log FilesRemote Host FieldDate/Time FieldHTTP Request FieldStatus Code FieldTransfer Volume (Bytes) FieldCommon Log FormatIdentification FieldAuthuser FieldExtended Common Log FormatReferrer FieldUser Agent FieldExample of a Web Log RecordMicrosoft IIS Log FormatAuxiliary InformationReferencesExercises7 PREPROCESSING FOR WEB USAGE MININGNeed for Preprocessing the DataData Cleaning and FilteringPage Extension Exploration and FilteringDe-Spidering the Web Log FileUser IdentificationSession IdentificationPath CompletionDirectories and the Basket TransformationFurther Data Preprocessing StepsReferencesExercises8 EXPLORATORY DATA ANALYSIS FOR WEB USAGE MININGIntroductionNumber of Visit ActionsSession DurationRelationship between Visit Actions and Session DurationAverage Time per PageDuration for Individual PagesReferencesExercises9 MODELING FOR WEB USAGE MINING: CLUSTERING, ASSOCIATION, AND CLASSIFICATIONIntroductionModeling MethodologyDefinition of ClusteringThe BIRCH Clustering AlgorithmAffinity Analysis and the A Priori AlgorithmDiscretizing the Numerical Variables: BinningApplying the A Priori Algorithm to the CCSU Web Log DataClassification and Regression TreesThe C4.5 AlgorithmReferencesExercisesINDEX
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