数据挖掘：概念与技术(影印版)

图书标准信息

• 作者 [美]韩（Han J.） 著
• 出版社 高等教育出版社
• 出版时间 2001-05
• 版次 1
• ISBN 9787040100419
• 定价 35.00元
• 装帧 平装
• 开本 16开
• 纸张 胶版纸
• 页数 550页
• 字数 762千字
• 正文语种 英语

【内容简介】

　　本书阐述了数据挖掘（通常称为数据库知识发现）的概念、方法和应用。从强调数据分析入手，介绍了数据库和数据挖掘的概念，指出数据挖掘是对大型数据库、数据构件库和其他大型信息资源中标识知识含义的那些类型的自动的或便捷的提取，并通过一个通用的框架回顾了当前的市场可供产品。数据挖掘是一个跨学科的知识领域，汲取了数据库技术、人工智能、机器学习、神经网络、统计学、模式识别、知识库系统、知识获取、信息检索、高性能计算、数据可视化等方面的成果，本书内容从数据库的视角，描述了数据挖掘系统的原型、结构、特征、方法，重点讲解了数据挖掘的可行性、实用性、有效性和大型数据库中模型发现的可测量性等问题。本书逐章讲解了数据分类、预测、联结和分组的概念和技术，这些专题都配有实例，对各类问题都分别列举了最佳算法，并对怎样运用技术给出了经过实践检验的实用型规则。这种讲述方式决定了本书的可读性强，能够使读者从中学到数据挖掘领域的知识，了解产业最新动向。本书适用于计算机科学系的学生、应用软件开发人员、商业领域的专家和相关知识领域的科技研究人员。
　　内容：1.数据挖掘简介2.数据构件库和数据挖掘中的在线分析处理技术3.数据处理4.数据挖掘原型、语言和系统结构5.概念描述：特征与对比6.大型数据库中的挖掘联结规则7.分类和预测8.分组分析9.挖掘复合数据类型10.数据挖掘应用及趋势附录一微软公司数据挖掘的对象链接和嵌入数据库附录二数据库挖掘器简介

【作者简介】

　　JiaweiHanisdirectoroftheIntelligentDatabaseSystemsresearchLaboratoryandprofessorintheSchoolofComputingScienceatSimonFraserUniversity.Welldnownforhisresearchintheareasofdatamininganddata-basesystems,hehasservedonprogramcommitteesfordozensofinternationalconferencesandworkshopsandoneditorialboardsforseveraljournals,includingIEEETransactionaonKnowledgeandDataEngineeringandDataMiningandKnowledgeDiscovery.
　　MichelineDamberisaresearcheradnfreelancetechnicalwriterwithanM.S.incomputerscience.SheisamemberoftheIntelligentDatabaseSystemsResearchLaboratoryatSimonFraserUniversity.

【目录】

Foreword
Preface
Chapter1Introduction
1．1WhatMotivatedDataMining?Whyisitimportant?
1．2So,WhatisDataMining?
1．3DataMining-OnWhatKindofData?
1．3．1RelationalDatabases
1．3．2DataWarehouses
1．3．3TransactionalDatabases
1．3．4AdvancedDatabaseSystemsandAdvancedDatabaseApplications
1．4DataMiningFunctionalities-WhatKindsofPatternsCanBeMined?
1．4．1Concept/ClassDescription：CharacterizationandDiscrimination
1．4．2AssociationAnalysis
1．4．3ClassificationandPrediction
1．4．4ClusterAnalysis
1．4．5OutlierAnalysis
1．4．6EvolutionAnalysis
1．5AreAllofthePatternsInteresting?
1．6ClassificationofDataMiningSystems
1．7MajorIssuesInDataMining
1．8Summary
Exercises
BibliographicNotes

Chapter2DataWarehouseandOLAPTechnologyforDataMining
2．1WhatisaDataWarehouse?
2．1．1DifferencesbetweenOperationalDatabaseSystemsandDataWarehouses
2．1．2But,WhyHaveaSeparateDataWarehouse?
2．2AMultidimensionalDataModel
2．2．1FromTablesandSpreadsheetstoDataCubes
2．2．2Stars,Snowflakes，andFactConstellations：SchemasforMultidimensionalDatabases
2．2．3ExamplesforDefiningStar,Snowflake,andFactConstellationSchemas
2．2．4Measures：TheirCategorizationandComputation
2．2．5IntroducingConceptHierarchies
2．2．6OLAPOperationsintheMultidimensionalDataModel
2．2．7AStarnetQueryModelforQueryingMultidimensionalDatabases
2．3DataWarehouseArchitecture
2．3．1StepsfortheDesignandConstructionOfDataWarehouses
2．3．2AThree-TierDataWarehouseArchitecture
2．3．3TypesofOLAPServers：ROLAPversusMOLAPversusHOLAP
2．4DataWarehouseImplementation
2．4．1EfficientComputationofDataCubes
2．4．2IndexingOLAPData
2．4．3EfficientProcessingofOLAPQueries
2．4．4MetadataRepository
2．4．5DataWarehouseBack-EndToolsandUtilities
2．5FurtherDevelopmentofDataCubeTechnology
2．5．1Discovery-DrivenExplorationofDataCubes
2．5．2ComplexAggregationatMultipleGranularities：MultifeatureCubes
2．5．3OtherDevelopments
2．6FromDataWarehousingtoDataMining
2．6．1DataWarehouseUsage
2．6．2FromOn-LineAnalyticalProcessingtoOn-LineAnalyticalMining
2．7Summary
Exercises
BibliographicNotes

Chapter3DataPreprocessing
3．1WhyPreprocesstheData?
3．2DataCleaning
3．2．1MissingValues
3．2．2NoisyData
3．2．3InconsistentData
3．3DataIntegrationandTransformation
3．3．1DataIntegration
3．3．2DataTransformation
3.4DataReduction
3.4.1DataCubeAggregation
3.4.2DimensionalityReduction
3.4.3DataCompression
3.4.4NumerosityReduction
3.5DiscretizationandConceptHierarchyGeneration
3.5.1DiscretizationandConceptHierarchyGenerationforNumeric
3.5.2ConceptHierarchyGenerationforCategoricalData
3.6Summary
Exercises
BibliographicNotes

Chapter4DataMiningPrimitives,Languages,andSystemArchitectures
4.1DataMiningPrimitives:WhatDefinesaDataMiningTask?
4.1.1Task-RelevantData
4.1.2TheKindofKnowledgetobeMined
4.1.3BackgroundKnowledge:ConceptHierarchies
4.1.4InterestingnessMeasures
4.1.5PresentationandVisualizationofDiscoveredPatterns
4.2ADataMiningQueryLanguage
4.2.1SyntaxforTask-RelevantDataSpecification
4,2.2SyntaxforSpecifyingtheKindofKnowledgetobeMined
4.2.3SyntaxforConceptHierarchySpecification
4.2.4SyntaxforInterestingnessMeasureSpecification
4.2.5SyntaxforPatternPresentationandVisualizationSpecification
4.2.6PuttingitAllTogether-AnExampleofaDMQLQuery
4.2.7OtherDataMiningLanguagesandtheStandardizationofDataMiningPrimitives
4.3DesigningGraphicalUserInterfacesBasedonaDataMiningQueryLanguage
4.4ArchitecturesofDataMiningSystems
4.5Summary
Exercises
BibliographicNotes

Chapter5ConceptDescription:CharacterizationandComparison
5.1WhatisConceptDescription?
5.2DataGeneralizationandSummarization-BasedCharacterization
5.2.1Attribute-OrientedInduction
5.2.2EfficientImplementationofAttribute-OrientedInduction
5.2.3PresentationoftheDerivedGeneralization
5.3AnalyticalCharacterization:AnalysisofAttributeRelevance
5.3.1WhyPerformAttributeRelevanceAnalysis?
5.3.2MethodsofAttributeRelevanceAnalysis
5.3.3AnalyticalCharacterization:AnExample
5.4MiningClassComparisons:DiscriminatingbetweenDifferentClasses
5.4.1ClassComparisonMethodsandImplementations
5.4.2PresentationofClassComparisonDescriptions
5.4.3ClassDescription:PresentationofBothCharacterizationandComparison
5.5MiningDescriptiveStatisticalMeasuresinLargeDatabases
5.5.1MeasuringtheCentralTendency
5.5.2MeasuringtheDispersionofData
5.5.3GraphDisplaysofBasicStatisticalClassDescriptions
5.6Discussion
5.6.1ConceptDescription:AComparisonwithTypicalMachineLearningMethods
5.6.2IncrementalandParallelMiningofConceptDescription
5.7Summary
Exercises
BibliographicNotes

Chapter6MiningAssociationRulesinLargeDatabases
6.1AssociationRuleMining
6.1.1MarketBasketAnalysis:AMotivatingExampleforAssociationRuleMining
6.1.2BasicConcepts
6.1.3AssociationRuleMining:ARoadMap
6.2MiningSingle-DimensionalBooleanAssociationRulesfromTransactionalDatabases
6.2.1TheAprioriAlgorithm:FindingFrequentItemsetsUsingCandidateGeneration
6.2.2GeneratingAssociationRulesfromFrequentItemsets
6.2.3ImprovingtheEfficiencyofApriori
6.2.4MiningFrequentItemsetswithoutCandidateGeneration
6.2.5IcebergQueries
6.3MiningMultilevelAssociationRulesfromTransactionDatabases
Chapter7ClassificationandPrediction
Chapter8ClusterAnalysis
Chapter9MiningComplexTypesofData
Chapter10ApplicationsandTrendsinDataMining
AppendixAAnIntroductiontoMicrosoftsOLEDBforDataMining
AppendixBAnIntroductiontoDBMiner