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Mining the Web

Book Description

Mining the Web: Discovering Knowledge from Hypertext Data is the first book devoted entirely to techniques for producing knowledge from the vast body of unstructured Web data. Building on an initial survey of infrastructural issues—including Web crawling and indexing—Chakrabarti examines low-level machine learning techniques as they relate specifically to the challenges of Web mining. He then devotes the final part of the book to applications that unite infrastructure and analysis to bring machine learning to bear on systematically acquired and stored data. Here the focus is on results: the strengths and weaknesses of these applications, along with their potential as foundations for further progress. From Chakrabarti's work—painstaking, critical, and forward-looking—readers will gain the theoretical and practical understanding they need to contribute to the Web mining effort.

* A comprehensive, critical exploration of statistics-based attempts to make sense of Web Mining.
* Details the special challenges associated with analyzing unstructured and semi-structured data.
* Looks at how classical Information Retrieval techniques have been modified for use with Web data.
* Focuses on today's dominant learning methods: clustering and classification, hyperlink analysis, and supervised and semi-supervised learning.
* Analyzes current applications for resource discovery and social network analysis.
* An excellent way to introduce students to especially vital applications of data mining and machine learning technology.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright
  4. Foreword
  5. Preface
  6. Table of Contents
  7. Chapter 1: Introduction
    1. 1.1 Crawling and Indexing
    2. 1.2 Topic Directories
    3. 1.3 Clustering and Classification
    4. 1.4 Hyperlink Analysis
    5. 1.5 Resource Discovery and Vertical Portals
    6. 1.6 Structured vs. Unstructured Data Mining
    7. 1.7 Bibliographic Notes
  8. Part I: Infrastructure
    1. Chapter 2: Crawling the Web
      1. 2.1 HTML and HTTP Basics
      2. 2.2 Crawling Basics
      3. 2.3 Engineering Large-Scale Crawlers
      4. 2.4 Putting Together a Crawler
      5. 2.5 Bibliographic Notes
    2. Chapter 3: Web Search and Information Retrieval
      1. 3.1 Boolean Queries and the Inverted Index
      2. 3.2 Relevance Ranking
      3. 3.3 Similarity Search
      4. 3.4 Bibliographic Notes
  9. Part II: Learning
    1. Chapter 4: Similarity and Clustering
      1. 4.1 Formulations and Approaches
      2. 4.2 Bottom-Up and Top-Down Partitioning Paradigms
      3. 4.3 Clustering and Visualization via Embeddings
      4. 4.4 Probabilistic Approaches to Clustering
      5. 4.5 Collaborative Filtering
      6. 4.6 Bibliographic Notes
    2. Chapter 5: Supervised Learning
      1. 5.1 The Supervised Learning Scenario
      2. 5.2 Overview of Classification Strategies
      3. 5.3 Evaluating Text Classifiers
      4. 5.4 Nearest Neighbor Learners
      5. 5.5 Feature Selection
      6. 5.6 Bayesian Learners
      7. 5.7 Exploiting Hierarchy among Topics
      8. 5.8 Maximum Entropy Learners
      9. 5.9 Discriminative Classification
      10. 5.10 Hypertext Classification
      11. 5.11 Bibliographic Notes
    3. Chapter 6: Semisupervised Learning
      1. 6.1 Expectation Maximization
      2. 6.2 Labeling Hypertext Graphs
      3. 6.3 Co-training
      4. 6.4 Bibliographic Notes
  10. Part III: Applications
    1. Chapter 7: Social Network Analysis
      1. 7.1 Social Sciences and Bibliometry
      2. 7.2 PageRank and HITS
      3. 7.3 Shortcomings of the Coarse-Grained Graph Model
      4. 7.4 Enhanced Models and Techniques
      5. 7.5 Evaluation of Topic Distillation
      6. 7.6 Measuring and Modeling the Web
      7. 7.7 Bibliographic Notes
    2. Chapter 8: Resource Discovery
      1. 8.1 Collecting Important Pages Preferentially
      2. 8.2 Similarity Search Using Link Topology
      3. 8.3 Topical Locality and Focused Crawling
      4. 8.4 Discovering Communities
      5. 8.5 Bibliographic Notes
    3. Chapter 9: The Future of Web Mining
      1. 9.1 Information Extraction
      2. 9.2 Natural Language Processing
      3. 9.3 Question Answering
      4. 9.4 Profiles, Personalization, and Collaboration
  11. References
  12. Index
  13. About the Author
  14. Instructions for online access