Web Data Mining

Exploring Hyperlinks, Contents, and Usage Data

  • Bing┬áLiu
Part of the Data-Centric Systems and Applications book series (DCSA)

Table of contents

  1. Front Matter
    Pages I-XX
  2. Bing Liu
    Pages 1-14
  3. Data Mining Foundations

    1. Front Matter
      Pages 15-15
    2. Bing Liu
      Pages 63-132
    3. Bing Liu
      Pages 133-169
    4. Bing Liu, Wee Sun Lee
      Pages 171-208
  4. Web Mining

    1. Front Matter
      Pages 209-209
    2. Bing Liu
      Pages 211-268
    3. Bing Liu
      Pages 269-309
    4. Bing Liu, Filippo Menczer
      Pages 311-362
    5. Bing Liu
      Pages 425-458
    6. Bing Liu
      Pages 459-526
    7. Bing Liu, Bamshad Mobasher, Olfa Nasraoui
      Pages 527-603
  5. Back Matter
    Pages 605-622

About this book

Introduction

Web mining aims to discover useful information and knowledge from Web hyperlinks, 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 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 the essential 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 as lecture slides, datasets, and implemented algorithms are available online.

Keywords

Information Integration Information Retrieval Machine Learning Opinion Mining Pattern Mining Recommender Systems Schema Matching Semi-Supervised Learning Social Network Analysis Structured Data Extraction Unsupervised Learning Web Crawling Web Data Mining Web Link Analysis Web Search Web Usage Mining Wrapper Generation

Authors and affiliations

  • Bing┬áLiu
    • 1
  1. 1.Dept. Computer ScienceUniversity of Illinois, ChicagoChicagoUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-19460-3
  • Copyright Information Springer-Verlag Berlin Heidelberg 2011
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-642-19459-7
  • Online ISBN 978-3-642-19460-3
  • About this book