《Web数据挖掘：挖掘Web内容模式、结构和用途》文字版[PDF]
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电驴资源
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| [Web数据挖掘：挖掘Web内容模式、结构和用途].(Data.Mining.the.Web.Uncovering.Patterns.in.Web.Content.Structure.and.Usage).Zdravko.Markov.Daniel.T.Larose.文字版.pdf | 6.79 MB |
| 6.79 MB | |
电驴资源简介
| 资源介绍 |
| 语言: 英文 |
| 地区: 美国 |
| 图书fenlei: 网络 |
| 中文名: Web数据挖掘：挖掘Web内容模式、结构和用途 |
| 发行时间: 2007年04月01日 |
| 资源格式: PDF |
| 版本: 文字版 |
简介:  内容介绍： 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). 内容截图： 
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目录: 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 |