| Description |
1 online resource |
| Series |
Advanced information and knowledge processing |
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Advanced information and knowledge processing.
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| Contents |
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 |
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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 |
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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 |
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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 |
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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 |
| Summary |
Social media data contains our communication and online sharing, mirroring our daily life. This book looks at how we can use and what we can discover from such big data:Basic knowledge (data & challenges) on social media analyticsClustering as a fundamental technique for unsupervised knowledge discovery and data miningA class of neural inspired algorithms, based on adaptive resonance theory (ART), tackling challenges in big social media data clustering Step-by-step practices of developing unsupervised machine learning algorithms for real-world applications in social media domainAdaptive Resonance Theory in Social Media Data Clustering stands on the fundamental breakthrough in cognitive and neural theory, i.e. adaptive resonance theory, which simulates how a brain processes information to perform memory, learning, recognition, and prediction. It presents initiatives on the mathematical demonstration of ART's learning mechanisms in clustering, and illustrates how to extend the base ART model to handle the complexity and characteristics of social media data and perform associative analytical tasks. Both cutting-edge research and real-world practices on machine learning and social media analytics are included in the book and if you wish to learn the answers to the following questions, this book is for you:How to process big streams of multimedia data?How to analyze social networks with heterogeneous data?How to understand a user's interests by learning from online posts and behaviors?How to create a personalized search engine by automatically indexing and searching multimodal information resources? |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Online resource; title from PDF title page (EBSCO, viewed May 7, 2019) |
| Subject |
Big data.
|
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Data mining.
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Social media.
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Data Mining
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Social Media
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social media.
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COMPUTERS -- Data Processing.
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Big data
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Data mining
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Social media
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| Form |
Electronic book
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| Author |
Tan, Ah-Hwee, author.
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Wunsch, Donald C., II, author.
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| ISBN |
9783030029852 |
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3030029859 |
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