Data Mining the Web (PDF)

PREFACE. PART I: WEB STRUCTURE MINING. 1 INFORMATION RETRIEVAL AND WEB SEARCH. Web Challenges. Web Search Engines. Topic Directories. Semantic Web. Crawling the Web. Web Basics. Web Crawlers. Indexing and Keyword Search. Document Representation. Implementation Considerations. Relevance Ranking. Advanced Text Search. Using the HTML Structure in Keyword Search. Evaluating Search Quality. Similarity Search. Cosine Similarity. Jaccard Similarity. Document Resemblance. References. Exercises. 2 HYPERLINK-BASED RANKING. Introduction. Social Networks Analysis. PageRank. Authorities and Hubs. Link-Based Similarity Search. Enhanced Techniques for Page Ranking. References. Exercises. PART II: WEB CONTENT MINING. 3 CLUSTERING. Introduction. Hierarchical Agglomerative Clustering. k-Means Clustering. Probabilty-Based Clustering. Finite Mixture Problem. Classification Problem. Clustering Problem. Collaborative Filtering (Recommender Systems). References. Exercises. 4 EVALUATING CLUSTERING. Approaches to Evaluating Clustering. Similarity-Based Criterion Functions. Probabilistic Criterion Functions. MDL-Based Model and Feature Evaluation. Minimum Description Length Principle. MDL-Based Model Evaluation. Feature Selection. Classes-to-Clusters Evaluation. Precision, Recall, and F-Measure. Entropy. References. Exercises. 5 CLASSIFICATION. General Setting and Evaluation Techniques. Nearest-Neighbor Algorithm. Feature Selection. Naive Bayes Algorithm. Numerical Approaches. Relational Learning. References. Exercises. PART III: WEB USAGE MINING. 6 INTRODUCTION TO WEB USAGE MINING. Definition of Web Usage Mining. Cross-Industry Standard Process for Data Mining. Clickstream Analysis. Web Server Log Files. Remote Host Field. Date/Time Field. HTTP Request Field. Status Code Field. Transfer Volume (Bytes) Field. Common Log Format. Identification Field. Authuser Field. Extended Common Log Format. Referrer Field. User Agent Field. Example of a Web Log Record. Microsoft IIS Log Format. Auxiliary

Zdravko Markov, PhD, is Associate Professor of Computer
Science at Central Connecticut State University. The author of
three textbooks, Dr. Markov teaches undergraduate and graduate
courses in computer science and artificial intelligence. He is
currently a Principal Investigator (PI) in a National Science
Foundation-funded project designed to introduce machine
learning to undergraduates.

Daniel T. Larose, PhD, is Professor of Statistics in the
Department of Mathematical Sciences at Central Connecticut State
University. He is the author of three data mining books and a
forthcoming textbook in undergraduate statistics. He developed and
directs CCSU's DataMining@CCSU programs.

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