《数据挖掘概念与技术(原书第2版)》全面地讲述数据挖掘领域的重要知识和技术创新。在第1版内容相当全面的基础上，第2版展示了该领域的最新研究成果，例如挖掘流、时序和序列数据以及挖掘时间空间、多媒体、文本和Web数据。本书可作为数据挖掘和知识发现领域的教师、研究人员和开发人员的一本必读书。 《数据挖掘概念与技术(原书第2版)》第1版曾是受读者欢迎的数据挖掘专著，是一本可读性极佳的教材。第2版充实了数据挖掘领域研究新进展的题材，增加了讲述最新的数据挖掘方法的若干章节。本书适合作为高等院校计算机及相关专业高年级本科生的选修课教材，特别适合作为研究生的专业课教材。 海报：

本书全面介绍了数据挖掘，涵盖了五个主题：数据、分类、关联分析、聚类和异常检测。除异常检测外，每个主题都有两章。前一章涵盖基本概念、代表性算法和评估技术，而后一章讨论高级概念和算法。这样读者在透彻地理解数据挖掘的基础的同时，还能够了解更多重要的高级主题。 本书是明尼苏达大学和密歇根州立大学数据挖掘课程的教材，由于独具特色，正式出版之前就已经被斯坦福大学、得克萨斯大学奥斯汀分校等众多名校采用。 本书特色  与许多其他同类图书不同，本书将重点放在如何用数据挖掘知识解决各种实际问题。  只要求具备很少的预备知识——不需要数据库背景，只需要很少的统计学或数学背景知识。  书中包含大量的图表、综合示例和丰富的习题，并且使用示例、关键算法的简洁描述和习题，尽可能直接地聚焦于数据挖掘的主要概念。  教辅内容极为丰富，包括课程幻灯片、学生课题建议、数据挖掘资源（如数据挖掘算法和数据集）、联机指南（使用实际的数据集和数据分析软件，为本书介绍的部分数据挖掘技术提供例子讲解）。  向采用本书作为教材的教师提供习题解答。

本书全面介绍了数据挖掘的理论和方法，旨在为读者提供将数据挖掘应用于实际问题所必需的知识。本书涵盖五个主题：数据、分类、关联分析、聚类和异常检测。除异常检测外，每个主题都包含两章：前面一章讲述基本概念、代表性算法和评估技术，后面一章较深入地讨论高级概念和算法。目的是使读者在透彻地理解数据挖掘基础的同时，还能了解更多重要的高级主题。此外，书中还提供了大量示例、图表和习题。 本书适合作为相关专业高年级本科生和研究生数据挖掘课程的教材，同时也可作为数据挖掘研究和应用开发人员的参考书。

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

本书是数据挖掘领域的经典著作，数年来畅销不衰。全书从技术和应用两个方面，全面、系统地介绍了数据挖掘的商业环境、数据挖掘技术及其在商业环境中的应用。自从1997年本书第1版出版以来，数据挖掘界发生了巨大的变化，其中的大部分核心算法仍然保持不变，但是算法嵌入的软件、应用算法的数据库以及用于解决的商业问题都有所演进。第2版展示如何利用基本的数据挖掘方法和技术，解决常见的商业问题。 本书涵盖核心的数据挖掘技术，包括：决策树、神经网络、协同过滤、关联规则、链接分析、聚类和生存分析等。此外，还提供了数据挖掘最佳实践、数据挖掘的最新进展和一些富有挑战性的研究课题，极具技术深度与广度。配套网站www．data-miners．com／companion提供了每章的练习和用于测试各种数据挖掘技术的数据。全书语句凝炼、清新，对复杂概念的实际应用进行了生动解释，是必不可少的数据挖掘教材。

As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights for the new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; plus much more; algorithmic methods at the heart of successful data mining-including tried and true techniques as well as leading edge methods; performance improvement techniques that work by transforming the input or output; and, downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization-in a new, interactive interface.

The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it's still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge. Since the previous edition's publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. * Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects. * Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields. *Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data

●全面实用地论述了从实际业务数据中抽取出的读者需要知道的概念和技术。 ●更新并结合了来自读者的反馈、数据挖掘领域的技术变化以及统计和机器学习方面的更多资料。 ●包含了许多算法和实现示例，全部以易于理解的伪代码编写，适用子实际的大规模数据挖掘项目。

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.