Mining the Web discovering knowledge from hypertext data
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 m...
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
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San Francisco, CA :
Morgan Kaufmann Publishers
c2003.
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| Edición: | 1st edition |
| Colección: | Morgan Kaufmann series in data management systems.
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009627937006719 |
Tabla de Contenidos:
- Front Cover; Mining the Web: Discovering Knowledge from Hypertext Data; Copyright Page; Foreword; Contents; Preface; 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 Retrieval3.1 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 Learners5.5 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 Distillation7.6 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; About the Author
