During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for "wide" data (p bigger than n), including multiple testing and false discovery rates.

Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each concept is explored thoroughly and supported with numerous examples. The text requires only a modest background in mathematics. Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms. Quotes This book provides a comprehensive coverage of important data mining techniques. Numerous examples are provided to lucidly illustrate the key concepts. -Sanjay Ranka, University of Florida In my opinion this is currently the best data mining text book on the market. I like the comprehensive coverage which spans all major data mining techniques including classification, clustering, and pattern mining (association rules). -Mohammed Zaki, Rensselaer Polytechnic Institute

数据挖掘领域最具里程碑意义的经典著作
完整全面阐述该领域的重要知识和技术创新
【编辑推荐】
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。最新的第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
【内容简介】
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。
本书前版曾被KDnuggets的读者评选为最受欢迎的数据挖掘专著，是一本可读性极佳的教材。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最新的课题——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，提供最佳算法，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。如果你希望自己能熟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内的所有教师、研究人员、开发人员和用户都必读的一本书。
本书特点
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。