Mining the Web Discovering Knowledge from Hypertext Data 1st Edition by Soumen Chakrabarti – Ebook PDF Instant Download/Delivery. 9780080511726 ,0080511724
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ISBN 10: 0080511724
ISBN 13: 9780080511726
Author: Soumen Chakrabarti
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.
Mining the Web Discovering Knowledge from Hypertext Data 1st Edition Table of contents:
Chapter 1. Introduction
1.1 Crawling and Indexing
1.2 Topic Directories
1.3 Clustering and Classification
1.4 Hyperlink Analysis
1.5 Resource Discovery and Vertical Portals
1.6 Structured vs. Unstructured Data Mining
1.7 Bibliographic Notes
Part I: Infrastructure
Chapter 2. Crawling the Web
2.1 HTML and HTTP Basics
2.2 Crawling Basics
2.3 Engineering Large-Scale Crawlers
2.4 Putting Together a Crawler
2.5 Bibliographic Notes
Chapter 3. Web Search and Information Retrieval
3.1 Boolean Queries and the Inverted Index
3.2 Relevance Ranking
3.3 Similarity Search
3.4 Bibliographic Notes
Part II: Learning
Chapter 4. Similarity And Clustering
4.1 Formulations and Approaches
4.2 Bottom-Up and Top-Down Partitioning Paradigms
4.3 Clustering and Visualization via Embeddings
4.4 Probabilistic Approaches to Clustering
4.5 Collaborative Filtering
4.6 Bibliographic Notes
Chapter 5. Supervised Learning
5.1 The Supervised Learning Scenario
5.2 Overview of Classification Strategies
5.3 Evaluating Text Classifiers
5.4 Nearest Neighbor Learners
5.5 Feature Selection
5.6 Bayesian Learners
5.7 Exploiting Hierarchy among Topics
5.8 Maximum Entropy Learners
5.9 Discriminative Classification
5.10 Hypertext Classification
5.11 Bibliographic Notes
Chapter 6. Semisupervised Learning
6.1 Expectation Maximization
6.2 Labeling Hypertext Graphs
6.3 Co-training
6.4 Bibliographic Notes
Part III: Applications
Chapter 7. Social Network Analysis
7.1 Social Sciences and Bibliometry
7.2 PageRank and HITS
7.3 Shortcomings of the Coarse-Grained Graph Model
7.4 Enhanced Models and Techniques
7.5 Evaluation of Topic Distillation
7.6 Measuring and Modeling the Web
7.7 Bibliographic Notes
Chapter 8. Resource Discovery
8.1 Collecting Important Pages Preferentially
8.2 Similarity Search Using Link Topology
8.3 Topical Locality and Focused Crawling
8.4 Discovering Communities
8.5 Bibliographic Notes
Chapter 9. The Future of Web Mining
9.1 Information Extraction
9.2 Natural Language Processing
9.3 Question Answering
9.4 Profiles, Personalization, and Collaboration
References
Index
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