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《数据挖掘:概念与技术(英文版·第2版)》((加)韩家炜,等

2018年06月01日 16:19  | 萬仟网互联网  | 我要评论

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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