Machine learning allows computational systems to adaptively improve their performance with experience accumulated from the observed data. Its techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we believe to be the core topics that every student of the subject should know. We chose the title `learning from data' that faithfully describes what the subject is about, and made it a point to cover the topics in a story-like fashion. Our hope is that the reader can learn all the fundamentals of the subject by reading the book cover to cover. ---- Learning from data has distinct theoretical and practical tracks. In this book, we balance the theoretical and the practical, the mathematical and the heuristic. Our criterion for inclusion is relevance. Theory that establishes the conceptual framework for learning is included, and so are heuristics that impact the performance of real learning systems. ---- Learning from data is a very dynamic field. Some of the hot techniques and theories at times become just fads, and others gain traction and become part of the field. What we have emphasized in this book are the necessary fundamentals that give any student of learning from data a solid foundation, and enable him or her to venture out and explore further techniques and theories, or perhaps to contribute their own. ---- The authors are professors at California Institute of Technology (Caltech), Rensselaer Polytechnic Institute (RPI), and National Taiwan University (NTU), where this book is the main text for their popular courses on machine learning. The authors also consult extensively with financial and commercial companies on machine learning applications, and have led winning teams in machine learning competitions.

This third edition of a classic textbook presents a comprehensive introduction to Bayesian data analysis. Written for students and researchers alike, the text is written in an easily accessible manner with chapters that contain many exercises as well as detailed worked examples taken from various disciplines. This third edition provides two new chapters on Bayesian nonparametrics and covers computation systems BUGS and R. It also offers enhanced computing advice. The book's website includes solutions to the problems, data sets, software advice, and other ancillary material.

Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet. With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it. Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains: * Collaborative filtering techniques that enable online retailers to recommend products or media * Methods of clustering to detect groups of similar items in a large dataset * Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm * Optimization algorithms that search millions of possible solutions to a problem and choose the best one * Bayesian filtering, used in spam filters for classifying documents based on word types and other features * Using decision trees not only to make predictions, but to model the way decisions are made * Predicting numerical values rather than classifications to build price models * Support vector machines to match people in online dating sites * Non-negative matrix factorization to find the independent features in a dataset * Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. "Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details." -- Dan Russell, Google "Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths." -- Tim Wolters, CTO, Collective Intellect

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

Description Contents Resources Courses About the Authors
This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data.

Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis.
This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs--a nascent but quickly growing subset of graph representation learning.

Written by leading authorities in database and Web technologies, this book is essential reading for students and practitioners alike. The popularity of the Web and Internet commerce provides many extremely large datasets from which information can be gleaned by data mining. This book focuses on practical algorithms that have been used to solve key problems in data mining and can be applied successfully to even the largest datasets. It begins with a discussion of the map-reduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream processing algorithms for mining data that arrives too fast for exhaustive processing. Other chapters cover the PageRank idea and related tricks for organizing the Web, the problems of finding frequent itemsets and clustering. This second edition includes new and extended coverage on social networks, machine learning and dimensionality reduction.

《SAS编程与数据挖掘商业案例》是作者多年来在企业实践工作中的经验总结，详细讲解了使用SAS进行商业数据挖掘的方法。其中包含了目前公开出版的诸多SAS教材没有的大量实战内容。《SAS编程与数据挖掘商业案例》内容全面、新颖独创、综合性强，适合企业人员使用，也可作为数学、统计学、金融、电子商务、医药等专业的本科生、硕士生学习SAS编程和数据挖掘的参考资料。

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。

支持向量机是数据挖掘中的一个新方法。支持向量机能非常成功地处理回归问题(时间序列分析)和模式识别(分类问题、判别分析)等诸多问题，并可推广于预测和综合评价等领域，因此可应用于理科、工科和管理等多种学科。目前国际上支持向量机在理论研究和实际应用两方面都正处于飞速发展阶段。希望本书能促进它在我国的普及与提高。
本书对象既包括关心理论的研究工作者，也包括关心应用的实际工作者。对于有关领域的具有高等数学知识的实际工作者，略去书中的某些理论部分，仍能对支持向量机的本质有一个概括的理解，从而用它解决自己的问题。
本书适合高等院校高年级学生、研究生、教师和相关科研人员及相关领域的实际工作者使用。

The growth of social media over the last decade has revolutionized the way individuals interact and industries conduct business. Individuals produce data at an unprecedented rate by interacting, sharing, and consuming content through social media. Understanding and processing this new type of data to glean actionable patterns presents challenges and opportunities for interdisciplinary research, novel algorithms, and tool development. Social Media Mining integrates social media, social network analysis, and data mining to provide a convenient and coherent platform for students, practitioners, researchers, and project managers to understand the basics and potentials of social media mining. It introduces the unique problems arising from social media data and presents fundamental concepts, emerging issues, and effective algorithms for network analysis and data mining. Suitable for use in advanced undergraduate and beginning graduate courses as well as professional short courses, the text contains exercises of different degrees of difficulty that improve understanding and help apply concepts, principles, and methods in various scenarios of social media mining.

《Web数据挖掘》旨在讲述这些任务以及它们的核心挖掘算法；尽可能涵盖每个话题的广泛内容，给出足够多的细节，以便读者无须借助额外的阅读，即可获得相对完整的关于算法和技术的知识。其中结构化数据的抽取、信息整合、观点挖掘和Web使用挖掘等4章是《Web数据挖掘》的特色，这些内容在已有书籍中没有提及，但它们在Web数据挖掘中却占有非常重要的地位。当然，传统的Web挖掘主题，如搜索、页面爬取和资源探索以及链接分析在书中也作了详细描述。
《Web数据挖掘》尽管题为“Web数据挖掘”，却依然涵盖了数据挖掘和信息检索的核心主题；因为Web挖掘大量使用了它们的算法和技术。数据挖掘部分主要由关联规则和序列模式、监督学习（分类）、无监督学习（聚类）这三大最重要的数据挖掘任务，以及半监督学习这个相对深入的主题组成。而信息检索对于Web挖掘而言最重要的核心主题都有所阐述。