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Jingdong book

经典原版书库·数据挖掘：概念与技术（英文版·第3版）

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Jingdong book

经典原版书库·数据挖掘：概念与技术（英文版·第3版）

Name: 经典原版书库·数据挖掘：概念与技术（英文版·第3版）
Brand: Jingdong book
SKU: 3118155751
Price: 57.03 USD
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《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。

Content Description

当代商业和科学领域大量激增的数据量要求我们采用更加复杂和精细的工具来进行数据分析、处理和挖掘尽管近年来数据挖掘技术取得的长足进展使得我们广泛收集数据越来越容易，但技术的发展依然难以匹配爆炸性的数据增长以及随之而来的大量数据处理需求，因此我们比以往更加迫切地需要新技术和自动化工具来帮助我们将这些数据转换为有用的信息和知识

《经曲原版书库·数据挖掘：概念与技术（英文版·第3版）》前版曾被KDnuggets的读者评选为受欢迎的数据挖掘专著，是一本可读性极好的教材它从数据库角度全面系统地介绍数据挖掘的概念、方法和技术以及技术研究进展，并重点关注近年来该领域重要和较新的课题--数据仓库和数据立方体技术，流数据挖掘，社会化网络挖掘，空间、多媒体和其他复杂数据挖掘每章都针对关键专题有单独的指导，提供很好算法，并对怎样将技术运用到实际工作中给出了经过实践检验的实用型规则如果你希望自己能秘练掌握和运用当今最有力的数据挖掘技术，那这本书正是你需要阅读和学习的宝贵资源本书是数据挖掘和知识发现领域声的所有教师、研究人员、开发人员和用户都必读的一本书。

Author Description

韩家炜，伊利诺伊大学厄巴纳-尚佩恩分校计算机科学系Abel Bliss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颁发的很好创新奖，2005年IEEE Computer Society 颁发的技术成就奖，2009年IEEE颁发的W. Wallace McDowell奖。他是ACM和IEEE Fellow，同时还是《ACM Transactions on Knowledge Discovery from Data》杂志的主编（2006-2011），以及《IEEE Transactions on Knowledge and Data Engineering》和《Data Mining and Knowledge 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，她是NSERC Scholar，现在加拿大麦吉尔大学、西蒙-弗雷泽大学及瑞士从事研究工作。

Jian Pei（裴健），目前是加拿大西蒙-弗雷泽大学计算机学院副教授。2002年，他在Jia wei Han教授的指导下获得西蒙-弗雷泽大学博士学位。

Comments

我们生活在数据洪流的时代。本书向我们展示了如何从这样海量的数据中找到有用知识的方法和技术。较新的第3版显著扩充了数据预处理、挖掘频繁模式、分类和聚类这几个核心章节的内容；还全面讲述了OLAP和离群点检测，并研讨了挖掘网络、复杂数据类型以及重要应用领域。本书将是一本适用于数据分析、数据挖掘和知识发现课程的优秀教材。

—— Gregory Piatetsky-Shapiro， KDnuggets的总裁

Jiawei、Micheline和Jian的教材全景式地讨论了数据挖掘的所有相关方法，从聚类和分类的经典主题，到数据库方法（关联规则、数据立方体），到更新和更高级的主题（SVD/PCA、小波、支持向量机），等等。总的说来，这是一本既讲述经典数据挖掘方法又涵盖大量当代数据挖掘技术的优秀著作，既是教学相长的优秀教材，又对专业人员具有很高的参考价值。

—— 摘自卡内基-梅隆大学Christos Faloutsos教授为本书所作序言

Catalogue

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

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