The first edition, published in 1973, has become a classic reference in the field. Now with the second edition, readers will find information on key new topics such as neural networks and statistical pattern recognition, the theory of machine learning, and the theory of invariances. Also included are worked examples, comparisons between different methods, extensive graphics, expanded exercises and computer project topics. An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.

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。

《国外经典教材•C++算法:图算法(第3版)》所关注的是图算法领域。从实用的视角，以独特的结构将有关内容组织在一起，从而使读者不仅可以对这一领域有系统性的认识，而且还可在实践中灵活使用所提供的算法工具。本版中，增加了数以千计的新练习、数百年新图表以及数十个新程序，而且对所有的图表和程序都做了详尽的注释说明；不仅涵盖了新的主题，还对许多经典算法提供了更为充分的解释。所有读者都可从中得到极为丰富的学习资料，从而更好地理解基本概念。

本书是计算机专业本科生或研究生知

《计算机图形学》与大多数传统的计算机图形学教材不同，它仅简要介绍交互式计算机图形学方面的基本知识，主要侧重于介绍计算机图形学在数学及其他科学领域的应用，解决实际问题。《计算机图形学》按照计算机图形学的传统顺序介绍视觉交流、视图变换和投影处理、建模、绘制、光照、着色处理，以及OpenGL API如何实现基本概念和技术，使学生理解并学会使用图形API实现图形操作，为观察者创造有效的图像。

《计算机程序设计艺术(第1卷):基本算法(英文版·第3版)》主要内容：关于算法分析的这多卷论著已经长期被公认为经典计算机科学的定义性描述。迄今已出版的完整的三卷已经组成了程序设计理论和实践的惟一的珍贵资源，无数读者都赞扬Knuth的著作对个人的深远影响，科学家们为他的分析的美丽和优雅所惊叹，而从事实践的程序员已经成功地将他的“菜谱式”的解应用到日常问题上，所有人都由于Knuth在书中表现出的博学、清晰、精确和高度幽默而对他无比敬仰。第1卷为基本算法，分“基本概念”和“信息结构”两章。本卷以基本的编程概念和技术开始，然后讲述信息结构——计算机内信息的表示法、数据元素间的结构关系以及处理它们的有效方法。

本书综合商业专业知识和数据挖掘模型开发于一体，系统地介绍了数据挖掘商业环境、数据挖掘技术及其在商业中的应用。在注重对数据挖掘技术讲解的同时，强调了数据挖掘在商业决策领域中的应用，弥补了大多数数据仓库技术类书籍商业应用不足的缺点。本书主线清晰，案例丰富，语言精练。 本书既可以作为商业专业本科生、研究生的教材，也可以在MBA、EMBA 教学和企业培训中使用。

"Introduction to Computing Systems: From bits & gates to C & beyond", now in its second edition, is designed to give students a better understanding of computing early in their college careers in order to give them a stronger foundation for later courses. The book is in two parts: the underlying structure of a computer, and programming in a high level language and programming methodology. To understand the computer, the authors introduce the LC-3 and provide the LC-3 Simulator to give students hands-on access for testing what they learn. To develop their understanding of programming and programming methodology, they use the C programming language.The book takes a "motivated" bottom-up approach, where the students first get exposed to the big picture and then start at the bottom and build their knowledge bottom-up. Within each smaller unit, the same motivated bottom-up approach is followed. Every step of the way, students learn new things, building on what they already know. The authors feel that this approach encourages deeper understanding and downplays the need for memorizing. Students develop a greater breadth of understanding, since they see how the various parts of the computer fit together.

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Sedgewick彻底修订和重写的C算法系列的第一本。全书分为四部分，共16章，第一部分“基础知识”（第1-2章）介绍基本算法分析原理。第二部分“数据结构”（第3-5章）讲解算法分析中必须掌握的数据结构知识，主要包括基本数据结构，抽象数据结构，递归和树。

本书是Sedgewick彻底修订和重写的丛书中的第二本，集中讲解图算法。全书共有6章（第17-22章）。第17章详细讨论图性质和类型，第18-22章分别讲解图搜索、有向图和DAG、最小生成树、最短路径以及网络流。 　　书中提供了用C语言描述的完整算法源程序，并且配有丰富插图和练习。作者用简洁的实现将理论和实践成功地结合了起来，这些实现均可在真实应用上测试，使得本书自问世以来备受程序员的欢迎。 本书可作为高等院校计算机相关专业算法与数据结构课程的教材和补充读物，也可供自学之用。

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.

Computer Vision is fast becoming an important technology and is used in Mars robots, national security systems, automated factories, driver-less cars, and medical image analysis to new forms of human-computer interaction. OpenCV is the most common library for computer vision, providing hundreds of complex and fast algorithms. But it has a steep learning curve and limited in-depth tutorials. Mastering OpenCV with Practical Computer Vision Projects is the perfect book for developers with just basic OpenCV skills who want to try practical computer vision projects, as well as the seasoned OpenCV experts who want to add more Computer Vision topics to their skill set or gain more experience with OpenCV’s new C++ interface before migrating from the C API to the C++ API. Each chapter is a separate project including the necessary background knowledge, so try them all one-by-one or jump straight to the projects you’re most interested in. Create working prototypes from this book including real-time mobile apps, Augmented Reality, 3D shape from video, or track faces & eyes, fluid wall using Kinect, number plate recognition and so on. Mastering OpenCV with Practical Computer Vision Projects gives you rapid training in nine computer vision areas with useful projects. - Allows anyone with basic OpenCV experience to rapidly obtain skills in many computer vision topics, for research or commercial use - Each chapter is a separate project covering a computer vision problem, written by a professional with proven experience on that topic - All projects include a step-by-step tutorial and full source-code, using the C++ interface of OpenCV

编码：隐匿在计算机软硬件背后的语言，ISBN：9787121181184，作者：（美）佩措尔德（Petzold,C.）著 左飞，薛佟佟译

《算法竞赛入门经典:训练指南》是《算法竞赛入门经典》的重要补充，旨在补充原书中没有涉及或者讲解得不够详细的内容，从而构建一个较完整的知识体系，并且用大量有针对性的题目，让抽象复杂的算法和数学具体化、实用化。《算法竞赛入门经典:训练指南》共6章，分别为算法设计基础、数学基础、实用数据结构、几何问题、图论算法与模型和更多算法专题，全书通过近200道例题深入浅出地介绍了上述领域的各个知识点、经典思维方式以及程序实现的常见方法和技巧，并在章末和附录中给出了丰富的分类习题，供读者查漏补缺和强化学习效果。

In this innovative book, Professor Robert Harper offers a fresh perspective on the fundamentals of programming languages through the use of type theory. Whereas most textbooks on this subject emphasize taxonomy, Harper instead emphasizes genetics, examining the building blocks from which all programming languages are constructed. The result is an introduction to programming theory that is both accessible and practical.

自己编写一个操作系统，是许多程序员的梦想。也许有人曾经挑战过，但因为太难而放弃了。其实你错了，你的失败并不是因为编写操作系统太难，而是因为没有人告诉你那其实是一件很简单的事。那么，你想不想再挑战一次呢？ 这是一本兼具趣味性、实用性与学习性的书籍。作者从计算机的构造、汇编语言、C语言开始解说，让你在实践中掌握算法。在这本书的指导下，从零编写所有代码，30天后就可以制作出一个具有窗口系统的32位多任务操作系统。 本书以课题为主导，边做边玩，抛开晦涩难懂的语言，行文风格十分随性，还充满了各种欢乐的吐槽，适合操作系统爱好者和程序设计人员阅读。

In Hacker's Delight, Second Edition, Hank Warren once again compiles an irresistible collection of programming hacks: timesaving techniques, algorithms, and tricks that help programmers build more elegant and efficient software, while also gaining deeper insights into their craft. Warren's hacks are eminently practical, but they're also intrinsically interesting, and sometimes unexpected, much like the solution to a great puzzle. They are, in a word, a delight to any programmer who is excited by the opportunity to improve. Extensive additions in this edition include * A new chapter on cyclic redundancy checking (CRC), including routines for the commonly used CRC-32 code * A new chapter on error correcting codes (ECC), including routines for the Hamming code * More coverage of integer division by constants, including methods using only shifts and adds * Computing remainders without computing a quotient * More coverage of population count and counting leading zeros * Array population count * New algorithms for compress and expand * An LRU algorithm * Floating-point to/from integer conversions * Approximate floating-point reciprocal square root routine * A gallery of graphs of discrete functions * Now with exercises and answers

Dive into Python's advanced possibilities, including algorithm analysis, graphs, scale-free networks, and cellular automata with this in-depth, hands-on guide. Whether you're an intermediate-level Python programmer, or a student of computational modeling, you'll examine data structures, complexity science, and other fascinating topics through a series of exercises, easy-to-understand explanations, and case studies. Think Complexity presents features that make Python such a simple and powerful language. Author Allen Downey provides code to help you get started, along with a solution for each exercise. With this book, you will: Work with graphs and graph algorithms, NumPy arrays and SciPy methods, basic signal processing and Fast Fourier Transform, and hash tables. Discover complexity science, the field that studies abstract models of complex physical systems, including power laws, fractals and pink noise, and Turing machines. Explore the philosophy of science through the models and results in this book about the nature of scientific laws, theory choice, and realism and instrumentalism, and more.