当代商业和科学领域大量激增的数据量要求我们采用更加复杂和精细的工具来进行数据分析、处理和挖掘尽管近年来数据挖掘技术取得的长足进展使得我们广泛收集数据越来越容易，但技术的发展依然难以匹配爆炸性的数据增长以及随之而来的大量数据处理需求，因此我们比以往更加迫切地需要新技术和自动化工具来帮助我们将这些数据转换为有用的信息和知识《经曲原版书库�数据挖掘：概念与技术（英文版�第3版）》前版曾被KDnuggets的读者评选为受欢迎的数据挖掘专著，是一本可读性极好的教材它从数据库角度全面系统地介绍数据挖掘的概念、方法和技术以及技术研究进展，并重点关注近年来该领域重要和较新的课题--数据仓库和数据立方体技术，流数据挖掘，社会化网络挖掘，空间、多媒体和其他复杂数据挖掘每章都针对关键专题有单独的指导，提供很好算法，并对怎样将技术运用到实际工作中给出了经过实践检验的实用型规则如果你希望自己能秘练掌握和运用当今最有力的数据挖掘技术，那这本书正是你需要阅读和学习的宝贵资源本书是数据挖掘和知识发现领域声的所有教师、研究人员、开发人员和用户都必读的一本书。《经曲原版书库�数据挖掘：概念与技术（英文版�第3版）》特点：引入了许多算法和实现示例，全部以易于理解的伪代码编写，适用于实际的大规模数据挖掘项目。讨论了一些高级主题，例如挖掘面向对象的关系型数据库、空间数据库、多媒体数据库、时间序列数据库、文本数据库、万维网以及其他领域的应用等。全面而实用地给出用于从海量数据中获取尽可能多信息的概念和技术。ForewordtoSecondEditionPrefaceAcknowledgmentsAbouttheAuthorsChapter1IntroductionWhyDataMining?MovingtowardtheInformationAgeDataMiningastheEvolutionofInformationTechnologyWhatIsDataMining?WhatKindsofDataCanBeMined?DatabaseDataDataWarehousesTransactionalDataOtherKindsofDataWhatKindsofPatternsCanBeMined?Class/ConceptDescription:CharacterizationandDiscriminationMiningFrequentPatterns,Associations,andCorrelationsClassificationandRegressionforPredictiveAnalysisClusterAnalysisOutlierAnalysisAreAllPatternsInteresting?WhichTechnologiesAreUsed?StatisticsMachineLearningDatabaseSystemsandDataWarehousesInformationRetrievalWhichKindsofApplicationsAreTargeted?BusinessIntelligenceWebSearchEnginesMa

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