Web數據挖掘：挖掘Web內容模式、結構和用途

《Web數據挖掘：挖掘Web內容模式、結構和用途》是2007年Wiley Blackwell出版的圖書，作者是Zdravko Markov。

基本介紹

• 書名：Web數據挖掘：挖掘Web內容模式、結構和用途
• 作者：Zdravko Markov
• 出版社：Wiley Blackwell
• 出版時間：2007年04月01日

圖書信息

中文名: Web數據挖掘：挖掘Web內容模式、結構和用途

作者: Zdravko Markov

Daniel T. Larose

圖書分類: 網路

資源格式: PDF

版本: 文字版

出版社: Wiley Blackwell

發行時間: 2007年04月01日

地區: 美國

語言: 英文

圖書簡介

內容簡介

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)

內容截圖

目錄

• 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