Web mining aims to discover useful information and knowledge from Web hyperlinks, page contents, and usage data. Although Web mining uses many conventionaldata mining techniques, it is not purely an application of traditional data mining due to the semi-structured and unstructured nature of the Web data. The field has also developed many of its own algorithms and techniques. Liu has written a comprehensive text on Web mining, which consists of two parts. The first part covers the data mining and machine learning foundations, where all theessential concepts and algorithms of data mining and machine learning are presented. The second part covers the key topics of Web mining, where Web crawling, search, social network analysis, structured data extraction, information integration, opinion mining and sentiment analysis, Web usage mining, query log mining, computational advertising, and recommender systems are all treated both in breadth and in depth. His book thus brings all the related concepts and algorithms together to form an authoritative and coherent text. The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in Web mining and data mining both as a learning text and as a reference book. Professors can readily use it for classes on data mining, Web mining, and text mining. Additional teaching materials such aslecture slides, datasets, and implemented algorithms are available online. Covers all key tasks and techniques of Web search and Web mining, i.e., structure mining, content mining, and usage mining. Includes major algorithms from data mining, machine learning, information retrieval and text processing, which are crucial for many Web mining tasks. Contains a rich blend of theory and practice, addressing seminal research ideas and also looking at the technology from a practical point of view. Second edition includes new/revised sections on supervised learning, opinion mining and sentiment analysis, recommender systems and collaborative filtering, and query log mining. Ideally suited for classes on data mining, Web mining, Web search, and knowledge discovery in databases. Provides internet support with lecture slides and project problems. INDICE: 1. Introduction. Part I: Data Mining Foundations. 2. Association Rules and Sequential Patterns. 3. Supervised Learning. 4. Unsupervised Learning. 5. Partially Supervised Learning. Part II: Web Mining. 6. Information Retrieval and Web Search. 7. Social Network Analysis. 8. Web Crawling. 9. StructuredData Extraction: Wrapper Generation. 10. Information Integration. 11. OpinionMining and Sentiment Analysis. 12. Web Usage Mining.
- ISBN: 978-3-642-26891-5
- Editorial: Springer Berlin Heidelberg
- Encuadernacion: Rústica
- Páginas: 600
- Fecha Publicación: 01/06/2011
- Nº Volúmenes: 1
- Idioma: Inglés