Data Warehousing and Data Mining

Name: Data Warehousing and Data Mining
ISBN: 9787301143131

廖开际Peking University Press · ChinaAug 2025

3.6

Word count: 181K words
Chapters: 74
ISBN: 9787301143131

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Summary

This book systematically introduces data warehouse principles, online analytical processing, data warehouse design and development, and decision-support applications of data warehouses; data mining principles, information-theoretic decision tree methods, set-theoretic rough set methods, association rules, formula discovery, neural networks, genetic algorithms, text mining and Web mining; and the development of data warehouses and data mining. It can be used as a college textbook and as a reference for researchers and engineering technicians in related fields.

Editor's review

This book presents data warehousing and data mining within a single framework, first explaining data warehouse principles, online analytical processing, and design and development, then extending to decision-support applications, forming a complete chain from data organization to knowledge discovery. Its strength lies in broad method coverage: it includes classic content such as information-theoretic decision trees, set-theoretic rough sets, association rules, and formula discovery, as well as directions such as neural networks, genetic algorithms, text mining, and Web mining, helping readers build a relatively complete knowledge map. As a college textbook, its structure suits semester-long progression, and teachers can arrange lectures and labs accordingly; for researchers and engineering technicians, the book's organization of principles and methods can also serve as a reference. Note that the book emphasizes method principles and systematic introduction; practical work needs to be supplemented with course design or real data projects to turn methods into hands-on ability.

Who it's for

A good fit for

Undergraduates in data science, computer science, information management, and related majors who need systematic coursework in data warehousing and mining
College teachers of data warehouse or data mining courses who need a textbook with complete structure and broad method coverage
Engineering technicians engaged in data analysis and decision support who need to supplement method principles and systematic knowledge
Readers preparing to enter data-related research and hoping to build a complete knowledge framework

Not a good fit for

Readers who only want to quickly master a particular data analysis tool and seek ready-to-use operational tips
Complete beginners without database or programming foundations who seek zero-threshold entry
Readers expecting many complete practical cases and end-to-end project exercises

Reading tips

First read through the data warehouse section to clarify the architecture and the role of online analytical processing, then move to the data mining method chapters.
Take notes by method category, grouping decision trees, rough sets, association rules, etc. into one comparative framework to distinguish similarities and differences.
After learning each type of method, implement or reproduce it on a small dataset to test your understanding of the principles.
For neural networks and genetic algorithms, review the relevant mathematical foundations first, then return to the book's derivations and algorithm steps.

About the publisher

Peking University Press China · Founded in 1979

Peking University Press is a comprehensive university press whose publishing areas cover classical literature, popular science, social culture, world history, Western philosophy, and culture. Its catalogue contains 6,031 titles, with representative books including A Dream of Red Mansions, Relativity: The Special and General Theory (15th Edition) (with Einstein's classic papers on unified field theory and other topics), Elements, Conics, The Aristocratic Families of the Eastern Jin, The Philosophy of Mythology (Volume I), The Philosophy of Mythology (Volume II), and The Origin of Continents and Oceans. Representative authors include Cao Xueqin, Zhou Ruchang, Albert Einstein, Euclid, Apollonius, Tian Yuqing, Schelling, and Xian Gang. The press has developed distinctive strengths in classical Chinese literature and in the translation of Western philosophical classics, while also covering scientific classics and social and cultural studies. As public information is limited, this description is based only on the books in the catalogue and offers no further extrapolation.

版权信息
专家编审委员会
丛书序
前言
第1章企业数据资源管理
1.1 数据资源的概念
1.2 数据资源管理的意义
1.3 信息资源管理的相关技术
1.4 企业通过数据仓库与数据挖掘获得竞争优势
本章小结
思考与练习
第2章数据仓库的概念与结构
2.1 数据仓库的概念
2.2 数据仓库系统
2.3 数据仓库中的数据组织
本章小结
思考与练习
第3章数据仓库的设计与开发
3.1 数据仓库的开发过程及特点
3.2 数据模型设计
3.3 数据仓库的粒度设计
3.4 创建数据仓库的基本步骤
本章小结
思考与练习
第4章联机分析处理
4.1 OLAP的基本概念
4.2 OLAP与多维分析
4.3 OLAP的分类
4.4 OLAP的特性与不足
4.5 SQL Server 2005统一维度模型
本章小结
思考与练习
第5章数据挖掘概述
5.1 数据挖掘技术的由来
5.2 数据挖掘的定义
5.3 数据挖掘发现的知识类型
5.4 数据挖掘流程
5.5 数据挖掘的方法和技术
本章小结
思考与练习
第6章数据预处理
6.1 数据预处理的目的及方法
6.2 数据清理
6.3 数据集成
6.4 数据变换
6.5 数据归约
本章小结
思考与练习
第7章数据挖掘中的常用算法
7.1 Apriori算法
7.2 决策树算法
7.3 神经网络算法
7.4 聚类分析
本章小结
思考与练习
第8章 SQL Server数据仓库与数据挖掘工具及其应用
8.1 SQL Server 2005的功能构架
8.2 SQL Server数据仓库设计与数据挖掘准备
8.3 SQL Server集成服务
8.4 SQL Server分析服务
8.5 SQL Server中的数据挖掘工具与应用
8.6 SQL Server报表服务
本章小结
思考与练习
附录A 一个简易的数据挖掘工具——Weka
A1 启动Weka
A2 Weka Explorer
A3 数据预处理(Preprocess)
A4 分类(Classify)
A5 聚类(Cluster)
A6 关联分析(Associate)
A7 选择属性和可视化
参考文献
北京大学出版社财经管理类实用规划教材（已出版）

FAQ

Can I read this book directly without a data mining background?

Yes, but some preparation is needed. The book starts from data warehouse principles and presents methods by category, making it suitable for systematic introductory study. However, it involves information theory, set theory, neural networks, and other content; it is advisable to have basic knowledge of probability and statistics, linear algebra, and programming, and to review alongside mathematical derivations for smoother reading.

Is this book more theoretical or more practical?

Judging from its content, the book mainly introduces principles, methods, and systems, covering data warehouse design and development, online analytical processing, and various mining methods—a typical college textbook approach. Practical work needs to be supplemented by readers through course design, labs, or real data projects; reading alone cannot provide complete hands-on experience.

There are many methods in the book—how should I choose among them when studying?

You can first master the data warehouse architecture and online analytical processing, then learn classic mining methods such as decision trees and association rules to form the main trunk; rough sets, formula discovery, genetic algorithms, etc. can be read as extensions according to interest or research needs. Text mining and Web mining are suitable after mastering basic methods, to understand how methods extend to new scenarios.

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About the author

廖开际 Author · China

Liao Kaiji is a Chinese author about whom limited public information is available. He is known for the book Data Warehousing and Data Mining, which is classified under general computer science. Judging from the subject matter of this work, Liao Kaiji focuses on the applied field of data warehousing and data mining, covering areas such as data organization, storage, and analytical use. Books of this kind are typically intended for university students in computer science and related majors, and may also serve as reference material for technical professionals engaged in data processing. Although background details such as the author's education, career, and research interests are not sufficiently documented, the book itself addresses a practical subject at the intersection of databases and data science and offers some reference value among Chinese computer science textbooks and professional readings.