Foundation / Part I: |
Introduction / 1: |
Background / 1.1: |
Data Mining and Web Mining / 1.2: |
Web Community and Social Network Analysis / 1.3: |
Characteristics of Web Data / 1.3.1: |
Web Community / 1.3.2: |
Social Networking / 1.3.3: |
Summary of Chapters / 1.4: |
Audience of This Book / 1.5: |
Theoretical Backgrounds / 2: |
Web Data Model / 2.1: |
Textual, Linkage and Usage Expressions / 2.2: |
Similarity Functions / 2.3: |
Correlation-based Similarity / 2.3.1: |
Cosine-Based Similarity / 2.3.2: |
Eigenvector, Principal Eigenvector / 2.4: |
Singular Value Decomposition (SVD) of Matrix / 2.5: |
Tensor Expression and Decomposition / 2.6: |
Information Retrieval Performance Evaluation Metrics / 2.7: |
Performance measures / 2.7.1: |
Web Recommendation Evaluation Metrics / 2.7.2: |
Basic Concepts in Social Networks / 2.8: |
Basic Metrics of Social Network / 2.8.1: |
Social Network over the Web / 2.8.2: |
Algorithms and Techniques / 3: |
Association Rule Mining / 3.1: |
Association Rule Mining Problem / 3.1.1: |
Basic Algorithms for Association Rule Mining / 3.1.2: |
Sequential Pattern Mining / 3.1.3: |
Supervised Learning / 3.2: |
Nearest Neighbor Classifiers / 3.2.1: |
Decision Tree / 3.2.2: |
Bayesian Classifiers / 3.2.3: |
Neural Networks Classifier / 3.2.4: |
Unsupervised Learning / 3.3: |
The k-Means Algorithm / 3.3.1: |
Hierarchical Clustering / 3.3.2: |
Density based Clustering / 3.3.3: |
Semi-supervised Learning / 3.4: |
Self-Training / 3.4.1: |
Co-Training / 3.4.2: |
Generative Models / 3.4.3: |
Graph based Methods / 3.4.4: |
Markov Models / 3.5: |
Regular Markov Models / 3.5.1: |
Hidden Markov Models / 3.5.2: |
K-Nearest-Neighboring / 3.6: |
Content-based Recommendation / 3.7: |
Collaborative Filtering Recommendation / 3.8: |
Memory-based collaborative recommendation / 3.8.1: |
Model-based Recommendation / 3.8.2: |
Social Network Analysis / 3.9: |
Detecting Community Structure in Networks / 3.9.1: |
The Evolution of Social Networks / 3.9.2: |
Web Mining: Techniques and Applications / Part II: |
Web Content Mining / 4: |
Vector Space Model / 4.1: |
Web Search / 4.2: |
Activities on Web archiving / 4.2.1: |
Web Crawling / 4.2.2: |
Personalized Web Search / 4.2.3: |
Feature Enrichment of Short Texts / 4.3: |
Latent Semantic Indexing / 4.4: |
Automatic Topic Extraction from Web Documents / 4.5: |
Topic Models / 4.5.1: |
Topic Models for Web Documents / 4.5.2: |
Inference and Parameter Estimation / 4.5.3: |
Opinion Search and Opinion Spam / 4.6: |
Opinion Search / 4.6.1: |
Opinion Spam / 4.6.2: |
Web Linkage Mining / 5: |
Web Search and Hyperlink / 5.1: |
Co-citation and Bibliographic Coupling / 5.2: |
Co-citation / 5.2.1: |
Bibliographic Coupling / 5.2.2: |
PageRank and HITS Algorithms / 5.3: |
PageRank / 5.3.1: |
HITS / 5.3.2: |
Web Community Discovery / 5.4: |
Bipartite Cores as Communities / 5.4.1: |
Network Flow/Cut-based Notions of Communities / 5.4.2: |
Web Community Chart / 5.4.3: |
Web Graph Measurement and Modeling / 5.5: |
Graph Terminologies / 5.5.1: |
Power-law Distribution / 5.5.2: |
Power-law Connectivity of the Web Graph / 5.5.3: |
Bow-tie Structure of the Web Graph / 5.5.4: |
Using Link Information for Web Page Classification / 5.6: |
Using Web Structure for Classifying and Describing Web Pages / 5.6.1: |
Using Implicit and Explicit Links for Web Page Classification / 5.6.2: |
Web Usage Mining / 6: |
Modeling Web User Interests using Clustering / 6.1: |
Measuring Similarity of Interest for Clustering Web Users / 6.1.1: |
Clustering Web Users using Latent Semantic Indexing / 6.1.2: |
Web Usage Mining using Probabilistic Latent Semantic Analysis / 6.2: |
Probabilistic Latent Semantic Analysis Model / 6.2.1: |
Constructing User Access Pattern and Identifying Latent Factor with PLSA / 6.2.2: |
Finding User Access Pattern via Latent Dirichlet Allocation Model / 6.3: |
Latent Dirichlet Allocation Model / 6.3.1: |
Modeling User Navigational Task via LDA / 6.3.2: |
Co-Clustering Analysis of weblogs using Bipartite Spectral Projection Approach / 6.4: |
Problem Formulation / 6.4.1: |
An Example of Usage Bipartite Graph / 6.4.2: |
Clustering User Sessions and Web Pages / 6.4.3: |
Web Usage Mining Applications / 6.5: |
Mining Web Logs to Improve Website Organization / 6.5.1: |
Clustering User Queries from Web logs for Related Query / 6.5.2: |
Using Ontology-Based User Preferences to Improve Web Search / 6.5.3: |
Social Networking and Web Recommendation: Techniques and Applications / Part III: |
Extracting and Analyzing Web Social Networks / 7: |
Extracting Evolution of Web Community from a Series of Web Archive / 7.1: |
Types of Changes / 7.1.1: |
Evolution Metrics / 7.1.2: |
Web Archives and Graphs / 7.1.3: |
Evolution of Web Community Charts / 7.1.4: |
Temporal Analysis on Semantic Graph using Three-Way Tensor Decomposition / 7.2: |
Algorithms / 7.2.1: |
Examples of Formed Community / 7.2.3: |
Analysis of Communities and Their Evolutions in Dynamic Networks / 7.3: |
Motivation / 7.3.1: |
Algorithm / 7.3.2: |
Community Discovery Examples / 7.3.4: |
Socio-Sense: A System for Analyzing the Societal Behavior from Web Archive / 7.4: |
System Overview / 7.4.1: |
Web Structural Analysis / 7.4.2: |
Web Temporal Analysis / 7.4.3: |
Consumer Behavior Analysis / 7.4.4: |
Web Mining and Recommendation Systems / 8: |
User-based and Item-based Collaborative Filtering Recommender Systems / 8.1: |
User-based Collaborative Filtering / 8.1.1: |
Item-based Collaborative Filtering Algorithm / 8.1.2: |
Performance Evaluation / 8.1.3: |
A Hybrid User-based and Item-based Web Recommendation System / 8.2: |
Problem Domain / 8.2.1: |
Hybrid User and Item-based Approach / 8.2.2: |
Experimental Observations / 8.2.3: |
User Profiling for Web Recommendation Based on PLSA and LDA Model / 8.3: |
Recommendation Algorithm based on PLSA Model / 8.3.1: |
Recommendation Algorithm Based on LDA Model / 8.3.2: |
Combing Long-Term Web Achieves and Logs for Web Query Recommendation / 8.4: |
Combinational CF Approach for Personalized Community Recommendation / 8.5: |
CCF: Combinational Collaborative Filtering / 8.5.1: |
C-U and C-D Baseline Models / 8.5.2: |
CCF Model / 8.5.3: |
Conclusions / 9: |
Summary / 9.1: |
Future Directions / 9.2: |
References |