Web Data Mining [electronic resource] :Exploring Hyperlinks, Contents, and Usage Data / by Bing Liu.
by Liu, Bing [author.]; SpringerLink (Online service).
Material type:
BookSeries: Data-Centric Systems and Applications: Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2007.Description: XX, 532p. 177 illus. online resource.ISBN: 9783540378822.Subject(s): Computer science | Data mining | Information storage and retrieval systems | Artificial intelligence | Optical pattern recognition | Statistics | Computer Science | Information Storage and Retrieval | Statistics for Engineering, Physics, Computer Science, Chemistry & Geosciences | Data Mining and Knowledge Discovery | Pattern Recognition | Artificial Intelligence (incl. Robotics)DDC classification: 025.04 Online resources: Click here to access online | Item type | Current location | Call number | Status | Date due | Barcode |
|---|---|---|---|---|---|
| MAIN LIBRARY | QA75.5-76.95 (Browse shelf) | Available |
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| RG1-991 Biology und Epidemiology of Hormone Replacement Therapy | TK5105.5-5105.9 Secure Mobile Ad-hoc Networks and Sensors | QA8.9-QA10.3 Relations and Kleene Algebra in Computer Science | QA75.5-76.95 Web Data Mining | QP501-801 Bioactive Egg Compounds | QA75.5-76.95 Preserving Digital Information | QA241-247.5 Cohomology of Number Fields |
Data Mining Foundations -- Association Rules and Sequential Patterns -- Supervised Learning -- Unsupervised Learning -- Partially Supervised Learning -- Web Mining -- Information Retrieval and Web Search -- Link Analysis -- Web Crawling -- Structured Data Extraction: Wrapper Generation -- Information Integration -- Opinion Mining -- Web Usage Mining.
Web mining aims to discover useful information and knowledge from the Web hyperlink structure, page contents, and usage data. Although Web mining uses many conventional data mining techniques, it is not purely an application of traditional data mining due to the semistructured and unstructured nature of the Web data and its heterogeneity. It has also developed many of its own algorithms and techniques. Liu has written a comprehensive text on Web data mining. Key topics of structure mining, content mining, and usage mining are covered both in breadth and in depth. His book brings together all the essential concepts and algorithms from related areas such as data mining, machine learning, and text processing to form an authoritative and coherent text. The book offers a rich blend of theory and practice, addressing seminal research ideas, as well as examining the technology from a practical point of view. It is suitable for students, researchers and practitioners interested in Web mining both as a learning text and a reference book. Lecturers can readily use it for classes on data mining, Web mining, and Web search. Additional teaching materials such as lecture slides, datasets, and implemented algorithms are available online.
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