《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。
Foreword to Second Edition
Preface
Acknowledgments
About the Authors
Chapter1 Introduction
Why Data Mining?
Moving toward the Information Age
Data Mining as the Evolution of Information Technology
What Is Data Mining?
What Kinds of Data Can Be Mined?
Database Data
Data Warehouses
Transactional Data
Other Kinds of Data
What Kinds of Patterns Can Be Mined?
Class/Concept Description: Characterization and Discrimination
Mining Frequent Patterns, Associations, and Correlations
Classification and Regression for Predictive Analysis
Cluster Analysis
Outlier Analysis
Are All Patterns Interesting?
Which Technologies Are Used?
Statistics
Machine Learning
Database Systems and Data Warehouses
Information Retrieval
Which Kinds of Applications Are Targeted?
Business Intelligence
Web Search Engines
Major Issues in Data Mining
Mining Methodology
User Interaction
Efificiency and Scalability
Diversity of Database Types
Data Mining and Society
Summary
Exercises
Bibliographic Notes
Chapter 2 Getting to Know Your Data
Data Objects and Attribute Types
What Is an Attribute?
Nominal Attributes
Binary Attributes
Ordinal Attributes
Numeric Attributes
Discrete versus Continuous Attributes
Basic Statistical Descriptions of Data
Measuring the Central Tendency: Mean, Median, and Mode
Measuring the Dispersion of Data: Range, Quartiles, Variance,
Standard Deviation, and Interquartile Range
Graphic Displays of Basic Statistical Descriptions of Data
Data Visualization
PixeI-Oriented Visualization Techniques
Geometric Projection Visualization Techniques
Icon-Based Visualization Techniques
Hierarchical Visualization Techniques
Visualizing Complex Data and Relations
Measuring Data Similarity and Dissimilarity
Data Matrix versus Dissimilarity Matrix
Proximity Measures for Nominal Attributes
Proximity Measures for Binary Attributes
Dissimilarity of Numeric Data: Minkowski Distance
Proximity Measures for Ordinal Attributes
Dissimilarity for Attributes of Mixed Types
Cosine Similarity
Summary
Exercises
Bibliographic Notes
……
Chapter 3 Data Preprocessing
Chapter 4 Data Warehousing and Online Analytical Processin
Chapter 5 Data Cube Technology
Chapter 6 Mining Frequent Patterns, Associations, and Correlations: Basic Concepts and Methods
Chapter 7 Advanced Pattern Mining
Chapter 8 Classification: Basic Concepts
Chapter 9 Classification: Advanced Methods
Chapter 10 Cluster Analysis: Basic Concepts and I~ethods
Chapter 11 Advanced Cluster Analysis
Chapter 12 Outlier Detection
Chapter 13 Data Mining Trends and Research Frontiers
Bibliography
Index
包装很好,价格略贵
评分趁活动买非常棒,优惠力度非常大,很好的书,英文原版
评分算是数据挖掘中及其经典的书了,值得一看
评分纸张非常不错啊..排版非常好.你值得拥有.
评分非常经典的一本书,韩指导大作
评分我感觉原版比翻译版更有味道,你觉得呢
评分东西不错,快递超级快。给京东快递点赞。
评分经典书籍,推荐一下。学习这方面值得一买。
评分经典书籍,数据挖掘入门必备
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