Springer eBooks may be purchased by end-customers only and are sold without copy protection (DRM free). Instead, all eBooks include personalized watermarks. This means you can read the Springer eBooks across numerous devices such as Laptops, eReaders, and tablets.
You can pay for Springer eBooks with Visa, Mastercard, American Express or Paypal.
After the purchase you can directly download the eBook file or read it online in our Springer eBook Reader. Furthermore your eBook will be stored in your MySpringer account. So you can always re-download your eBooks.
Covers all key tasks and techniques of Web search and Web mining, i.e., structure mining, content mining, and usage mining
First book with such a comprehensive coverage
Includes major algorithms from data mining, machine learning, information retrieval and text processing, which are crucial for many Web mining tasks
First self-contained book
Contains a rich blend of theory and practice, addressing seminal research ideas and also looking at the technology from a practical point of view
Ideally suited for classes on Data Mining, Web Mining, Web Search, and Knowledge Discovery in Data Bases
Provides internet support with lecture slides and project problems
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 slid
Content Level »Professional/practitioner
Keywords »Perl - Web Crawling - Web Data Mining - algorithms - data mining - learning - machine learning - web mining
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.