Intro; Preface; Scope; Content; Audience; Acknowledgments; Contents; Theories; 1 Introduction; 1.1 Clustering in the Era of Web 2.0; 1.2 Research Issues and Challenges; 1.2.1 Representation of Social Media Data; 1.2.2 Scalability for Big Data; 1.2.3 Robustness to Noisy Features; 1.2.4 Heterogeneous Information Fusion; 1.2.5 Sensitivity to Input Parameters; 1.2.6 Online Learning Capability; 1.2.7 Incorporation of User Preferences; 1.3 Approach and Methodology; 1.4 Outline of the Book; References; 2 Clustering and Its Extensions in the Social Media Domain; 2.1 Clustering

2.1.1 K-Means Clustering2.1.2 Hierarchical Clustering; 2.1.3 Graph Theoretic Clustering; 2.1.4 Latent Semantic Analysis; 2.1.5 Non-Negative Matrix Factorization; 2.1.6 Probabilistic Clustering; 2.1.7 Genetic Clustering; 2.1.8 Density-Based Clustering; 2.1.9 Affinity Propagation; 2.1.10 Clustering by Finding Density Peaks; 2.1.11 Adaptive Resonance Theory; 2.2 Semi-Supervised Clustering; 2.2.1 Group Label Constraint; 2.2.2 Pairwise Label Constraint; 2.3 Heterogeneous Data Co-Clustering; 2.3.1 Graph Theoretic Models; 2.3.2 Non-Negative Matrix Factorization Models

2.3.3 Markov Random Field Model2.3.4 Multi-view Clustering Models; 2.3.5 Aggregation-Based Models; 2.3.6 Fusion Adaptive Resonance Theory; 2.4 Online Clustering; 2.4.1 Incremental Learning Strategies; 2.4.2 Online Learning Strategies; 2.5 Automated Data Cluster Recognition; 2.5.1 Cluster Tendency Analysis; 2.5.2 Posterior Cluster Validation Approach; 2.5.3 Algorithms Without a Pre-defined Number of Clusters; 2.6 Social Media Mining and Related Clustering Techniques; 2.6.1 Web Image Organization; 2.6.2 Multimodal Social Information Fusion; 2.6.3 User Community Detection in Social Networks

2.6.4 User Sentiment Analysis2.6.5 Event Detection in Social Networks; 2.6.6 Community Question Answering; 2.6.7 Social Media Data Indexing and Retrieval; 2.6.8 Multifaceted Recommendation in Social Networks; References; 3 Adaptive Resonance Theory (ART) for Social Media Analytics; 3.1 Fuzzy ART; 3.1.1 Clustering Algorithm of Fuzzy ART; 3.1.2 Algorithm Analysis; 3.2 Geometric Interpretation of Fuzzy ART; 3.2.1 Complement Coding in Fuzzy ART; 3.2.2 Vigilance Region (VR); 3.2.3 Modeling Clustering Dynamics of Fuzzy ART Using VRs; 3.2.4 Discussion

3.3 Vigilance Adaptation ARTs (VA-ARTs) for Automated Parameter Adaptation3.3.1 Activation Maximization Rule; 3.3.2 Confliction Minimization Rule; 3.3.3 Hybrid Integration of AMR and CMR; 3.3.4 Time Complexity Analysis; 3.3.5 Experiments; 3.4 User Preference Incorporation in Fuzzy ART; 3.4.1 General Architecture; 3.4.2 Geometric Interpretation; 3.5 Probabilistic ART for Short Text Clustering; 3.5.1 Procedures of Probabilistic ART; 3.5.2 Probabilistic Learning for Prototype Modeling; 3.6 Generalized Heterogeneous Fusion ART (GHF-ART) for Heterogeneous Data Co-Clustering