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，**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*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*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.

Foreword
Preface
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?
1.4 Data Mining Functionalities-What Kinds of Patterns Can Be Mined?
1.5 Are All of the Patterns Interesting?
1.6 Classification of Data Mining Systems
1.7 Major Issues in Data Mining
1.8 Summary
Exercises
Bibliographic Notes
Chapter2 Data Warehouse and LOAP Technology for Data Mining
2.1 What Is a Data Warehouse?
2.2 A Multidimensional Data Model
2.3 Data Warehouse Architecture
2.4 Data Warehouse Implementation
2.5 Further Development of Data Cube Technology
2.6 From Data Warehousing to Data Mining
2.7 Summary
Exercises
Bibliographic Notes
Chapter3 Data Preprocessing
3.1 Why Preprocess the Data?
3.2 Data Cleaning
3.3 Data Integration and Transformation
3.4 Data Reduction
3.5 Discretization and Concept Hierarchy Generation
3.6 Summary
Exercises
Bibliographic Notes
Chapter4 Data Mining Primitives，Languages，and System Architectures
Chapter5 Concept Description：Characterization and Comparison
Chapter6 Mining Association Rules in Large Databases
Chapter7 Classification and Prediction
Chapter8 Cluster Analysis
Chapter9 Mining Comples Types of Data
Chapter10 Applications and Trends in Data Mining
Appendix A Introduction to Microsoft’s OLE DB for Data Mining
Appendix B An Introduction to BDMiner
Bibliography
Index