The rapid growth of the Web in the past two decades has made it the largest publicly accessible data source in the world. Web mining aims to discover useful information or knowledge from Web hyperlinks, page contents, and usage logs. Based on the primary kinds of data used in the mining process, Web mining tasks can be categorized into three main types: Web structure mining, Web content mining and Web usage mining. Web structure mining discovers knowledge from hyperlinks, which represent the structure of the Web. Web content mining extracts useful information/ knowledge from Web page contents. Web usage mining mines user activity patterns from usage logs and other forms of logs of user interactions with Web systems. Since the publication of the first edition at the end of 2006, there have been some important advances in several areas. To reflect these advances, new materials have been added to most chapters. The major changes are in Chapter 11 and Chapter 12, which have been rewritten and significantly expanded. When the first edition was written, opinion mining (Chapter 11) was still in its infancy. Since then, the research community has gained a much better understanding of the problem and has proposed many novel techniques to solve various aspects of the problem. To include the latest developments for the Web usage mining chapter (Chapter 12), the topics of recommender systems and collaborative filtering, query log mining, and computational advertising have been added. This new edition is thus considerably longer, from a total of 532 pages in the first edition to a total of 622 pages in this second edition.

The goal of the book is to present the above Web data mining tasks and their core mining algorithms. The book is intended to be a text with a comprehensive coverage, and therefore, for each topic, sufficient details are given so that readers can gain a reasonably complete knowledge of its algorithms or techniques without referring to any external materials. Five of the chapters - partially supervised learning, structured data extraction, information integration, opinion mining and sentiment analysis, and Web usage mining - make this book unique. These topics are not covered by existing books, but yet are essential to Web data mining. Traditional Web mining topics such as search, crawling and resource discovery, and social network analysis are also covered in detail in this book.