标题：Web数据挖掘:超文本数据的知识发现
作者简介
SoumenChakrabarti，Web搜索与挖掘领域的知名专家，ACMTransactionsontheWeb副主编。加州大学伯克利分校博士，目前是印度理工学院计算机科学与工程系副教授。曾经供职于IBMAlmaden研究中心，从事超文本数据库和数据挖掘方面的工作。他有丰富的实际项目开发经验，开发了多个Web挖掘系统，并获得了多项美国专利。
编辑推荐
《Web数据挖掘》是Web挖掘与搜索引擎领域的经典著作，自出版以来深受好评，已经  被斯坦福、普林斯顿、卡内基梅隆等世界名校采用为教材。书中首先介绍了Web爬行和搜索等许多基础性的问题，并以此为基础，深入阐述了解决Web挖掘各种难题所涉及的机器学习技术，提出了机器学习在系统获取、存储和分析数据中的许多应用，并探讨了这些应用的优劣和发展前景。
《Web数据挖掘》分析透彻，富于前瞻性，为构建Web挖掘创新性应用奠定了理论和实践基础，既适用于信息检索和机器学习领域的研究人员和高校师生，也是广大Web开发人员的优秀参考书。
目录
INTRODUCTION
1.1CrawlingandIndexing
1.2TopicDirectories
1.3ClusteringandClassification
1.4HyperlinkAnalysis
1.5ResourceDiscoveryandVerticalPortals
1.6Structuredvs.UnstructuredDataMining
1.7BibliographicNotes
PARTⅠINFRASTRUCTURE
2CRAWLINGTHEWEB
2.1HTMLandHTTPBasics
2.2CrawlingBasics
2.3EngineeringLarge-ScaleCrawlers
2.3.1DNSCaching,Prefetching,andResolution
2.3.2MultipleConcurrentFetches
2.3.3LinkExtractionandNormalization
2.3.4RobotExclusion
2.3.5EliminatingAlready-VisitedURLs
2.3.6SpiderTraps
2.3.7AvoidingRepeatedExpansionofLinksonDuplicatePages
2.3.8LoadMonitorandManager
2.3.9Per-ServerWork-Queues
2.3.10TextRepository
2.3.11RefreshingCrawledPages
2.4PuttingTogetheraCrawler
2.4.1DesignoftheCoreComponents
2.4.2CaseStudy:Usingw3c-1ibwww
2.5BibliographicNotes
3WEBSEARCHANDINFORMATIONRETRIEVAL
3.1BooleanQueriesandtheInvertedIndex
3.1.1StopwordsandStemming
3.1.2BatchIndexingandUpdates
3.1.3IndexCompressionTechniques
3.2RelevanceRanking
3.2.1RecallandPrecision
3.2.2TheVector-SpaceModel
3.2.3RelevanceFeedbackandRocchio'sMethod
3.2.4ProbabilisticRelevanceFeedbackModels
3.2.5AdvancedIssues
3.3SimilaritySearch
3.3.1Handling"Find-Similar"Queries
3.3.2EliminatingNearDuplicatesviaShingling
3.3.3DetectingLocallySimilarSubgraphsoftheWeb
3.4BibliographicNotes
PARTⅡLEARNING
SIMILARITYANDCLUSTERING
4.1FormulationsandApproaches
4.1.1PartitioningApproaches
4.1.2GeometricEmbeddingApproaches
4.1.3GenerativeModelsandProbabilisticApproaches
4.2Bottom-UpandTop-DownPartitioningParadigms
4.2.1AgglomerativeClustering
4.2.2Thek-MeansAlgorithm
4.3ClusteringandVisualizationviaEmbeddings
4.3.1Self-OrganizingMaps(SOMs)
4.3.2MultidimensionalScaling(MDS)andFastMap
4.3.3ProjectionsandSubspaces
4.3.4LatentSemanticIndexing(LSI)
4.4ProbabilisticApproachestoClustering
4.4.1GenerativeDistributionsforDocuments
4.4.2MixtureModelsandExpectationMaximization(EM)
4.4.3MultipleCauseMixtureModel(MCMM)
4.4.4AspectModelsandProbabilisticLSI
4.4.5ModelandFeatureSelection
4.5CollaborativeFiltering
4.5.1ProbabilisticModels
4.5.2CombiningContent-BasedandCollaborativeFeatures
4.6BibliographicNotes
5SUPERVISEDLEARNING
5.1TheSupervisedLearningScenario
5.2OverviewofClassificationStrategies
5.3EvaluatingTextClassifiers
5.3.1Benchmarks
5.3.2MeasuresofAccuracy
5.4NearestNeighborLearners
5.4.1ProsandCons
5.4.2IsTFIDFAppropriate?
5.5FeatureSelection
5.5.1GreedyInclusionAlgorithms
5.5.2TruncationAlgorithms
5.5.3ComparisonandDiscussion
5.6BayesianLearners
5.6.1NaiveBayesLearners
5.6.2Small-DegreeBayesianNetworks
5.7ExploitingHierarchyamongTopics
5.7.1FeatureSelection
5.7.2EnhancedParameterEstimation
5.7.3TrainingandSearchStrategies
5.8MaximumEntropyLearners
5.9DiscriminativeClassification
5.9.1LinearLeast-SquareRegression
5.9.2SupportVectorMachines
5.10HypertextClassification
5.10.1RepresentingHypertextforSupervisedLearning
5.10.2RuleInduction
5.11BibliographicNotes
6SEMISUPERVISEDLEARNING
6.1ExpectationMaximization
6.1.1ExperimentalResults
6.1.2ReducingtheBeliefinUnlabeledDocuments
6.1.3ModelingLabelsUsingManyMixtureComponents
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PARTⅢAPPLICATIONS
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