| 《数据挖掘：概念与技术》（英文版第2版）适合作为高等院校计算及相关专业高年级本科生的选修课教材，特别适合作为研究生的专业课教材，同时也可供从事数数据挖掘研究和应用开发工作的相关人员作为必备的参考书。 《数据挖掘：概念与技术》（英文版第2版）主要特点是：全面实用地论述了从实际业务数据中抽取出的读者需要知道的概念和技术。更新并结合了来自读者的反馈、数据挖掘领域的技术变化以及统计和机器学习方面的更多资料。包含了许多算法和实际示例，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。 |
| Jiawei Han伊利诺伊大学厄巴纳一尚佩恩分校计算机科学系教授。由于在数据挖掘和数据库系统领域卓有成效的研究工作，他曾多次获得各种荣誉和奖励，其中包括2004年ACM SIGKDD颁发的创新奖。同时，他还是《ACM Trarlsactiorls on Krlowledge Discovery fronl Data》杂志的主编，以及《IEEE Trarlsactiorls 0n Krlowledge and Data Engirleering》和《Data Mirling and Krlowledge Discovery》杂志的编委会成员。 Micheline Kamber拥有加拿大康考迪亚大学计算机科学硕士学位，现在加拿大西蒙·弗雷泽大学从事博士后研究工作。 |
| Foreword vii Preface ix Chapter1 Introduction 1.1 What Motivated Data Mining? Why Is It Important? 1.2 So, What Is Data Mining? 1.3 Data Mining-On What Kind of Data? 1.3.1 Relational Databases 1.3.2 Data Warehouses 1.3.3 TransactionalDatabases 1.3.4 Advanced Data and Information Systems and Advanced Applications 1.4 Data Mining Functionalities---What Kinds of Patterns Can Be Mined? 1.4.1 Concept/Class Description: Characterization and Discrimination 1.4.2 Mining Frequent Patterns, Associations, and Correlations 1.4.3 Classification and Prediction 24 1.4.4 Cluster Analysis 1.4.5 Outlier Analysis 26 1.4.6 Evolution Analysis 1.5 Are All of the Patterns Interesting? 1.6 Classification of Data Mining Systems 1.7 Data Mining Task Primitives 1.8 Integration of a Data Mining System with a Database or Data Warehouse System 1.9 Major Issues in Data Mining 1.10 Summary Exercises Bibliographic Notes Chapter2 Data Preprocessing 2.1 Why Preprocess the Data? 2.2 Descriptive Data Summarization 2.2.1 Measuring the Central Tendency 2.2.2 Measuring the Dispersion of Data 2.2.3 Graphic Displays of Basic Descriptive Data Summaries 2.3 Data Cleaning 2.3.1 Missing Values 2.3.2 Noisy Data 2.3.3 Data Cleaning as a Process 2.4 Data Integration and Transformation 2.4.1 Data Integration 2.4.2 Data Transformation 2.5 Data Reduction 2.5.1 Data Cube Aggregation 2.5.2 Attribute Subset Selection 2.5.3 DimensionalityReduction 2.5.4 Numerosity Reduction 2.6 Data Discretization and Concept Hierarchy Generation 2.6.1 Discretization and Concept Hierarchy Generation for Numerical Data 2.6.2 Concept Hierarchy Generation for Categorical Data 2.7 Summary 97 Exercises 97 Bibliographic Notes Chapter3 Data Warehouse and OLAP Technology: An Overview 3.1 What Is a Data Warehouse? 3.1.1 Differences between Operational Database Systems and Data Warehouses 3.1.2 But, Why Have a Separate Data Warehouse? 3.2 A Multidimensional Data Model 3.2.1 From Tables and Spreadsheets to Data Cubes 3.2.2 Stars, Snowflakes, and Fact Constellations: Schemas for Multidimensional Databases 3.2.3 Examples for Defining Star, Snowflake, and Fact Constellation Schemas …… Chapter4 Data Cube Computation and Data Generalization Chapter5 Mining Frequent Patterns, Associations, and Correlations Chapter6 Classification adn Predidction Chapter7 Cluster Analysis Chapter8 Mining Stream, Time-Series, and Sepuence Data Chapter9 Graph Mining, Social Network Analysis, and Multirelational Chapter10 Mining Object, Spatial, Multimedia, Test, and Wed Data Chapter11 Applications and Trends in Data Mining An Introduction to Microsofts OLE DB for Bibliography Index |
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