Zdravko Markov, Daniel T. Larose

Data Mining the Web (eBook, PDF)

Uncovering Patterns in Web Content, Structure, and Usage

• Format: PDF

Produktbeschreibung

This book introduces the reader to methods of data mining on the web, including uncovering patterns in web content (classification, clustering, language processing), structure (graphs, hubs, metrics), and usage (modeling, sequence analysis, performance).

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 240
• Erscheinungstermin: 20. August 2007
• Englisch
• ISBN-13: 9780470108086
• Artikelnr.: 37290395

Autorenporträt

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.

Inhaltsangabe

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 Information.
References. Exercises. 7 PREPROCESSING FOR WEB USAGE MINING. Need for
Preprocessing the Data. Data Cleaning and Filtering. Page Extension
Exploration and Filtering. De-Spidering the Web Log File. User
Identification. Session Identification. Path Completion. Directories and
the Basket Transformation. Further Data Preprocessing Steps. References.
Exercises. 8 EXPLORATORY DATA ANALYSIS FOR WEB USAGE MINING. Introduction.
Number of Visit Actions. Session Duration. Relationship between Visit
Actions and Session Duration. Average Time per Page. Duration for
Individual Pages. References. Exercises. 9 MODELING FOR WEB USAGE MINING:
CLUSTERING, ASSOCIATION, AND CLASSIFICATION. Introduction. Modeling
Methodology. Definition of Clustering. The BIRCH Clustering Algorithm.
Affinity Analysis and the A Priori Algorithm. Discretizing the Numerical
Variables: Binning. Applying the A Priori Algorithm to the CCSU Web Log
Data. Classification and Regression Trees. The C4.5 Algorithm. References.
Exercises. INDEX.

Rezensionen

"I can say I really enjoyed reading this bookâ?¦a great educational resource for students and teachers." ( Information Retrieval , 2008)