Web Data Mining
Springer Berlin (Verlag)
978-3-540-37881-5 (ISBN)
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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.
Bing Liu is an associate professor in Computer Science at the University of Illinois at Chicago (UIC). He received his PhD degree in Artificial Intelligence from the University of Edinburgh. Before joining UIC in 2002, he was with the National University of Singapore. His research interests include data mining, Web mining, text mining, and machine learning. He has published extensively in these areas in leading conferences and journals. He served (or serves) as a vice chair, deputy vice chair or program committee member of many conferences, including WWW, KDD, ICML, VLDB, ICDE, AAAI, SDM, CIKM and ICDM.
1) Introduction
- 2) Association Rules and Sequential Patterns
- 3) Supervised Learning
- 4) Unsupervised Learning
- 5) Partially Supervised Learning
- 6) Information Retrieval and Web Search
- 7) Link Analysis
- 8) Web Crawling
- 9) Structured Data Extraction: Wrapper Generation
- 10) Information Integration
- 11) Opinion Mining
- 12) Web Usage Mining - References, Index
| Reihe/Serie | Data-Centric Systems and Applications |
|---|---|
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 955 g |
| Einbandart | gebunden |
| Themenwelt | Mathematik / Informatik ► Informatik |
| Schlagworte | Data Mining • Datenbankrecherche • information integration • Information Retrieval • Internet-Recherche • machine learning • Opinion Mining • pattern mining • Schema Matching • Semi-Supervised Learning • Unsupervised Learning • Web Crawling • Web Data Mining • Web link analysis • Web mining • Web Search • Web Usage Mining • Wrapper Generation |
| ISBN-10 | 3-540-37881-2 / 3540378812 |
| ISBN-13 | 978-3-540-37881-5 / 9783540378815 |
| Zustand | Neuware |
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