Adaptive Resonance Theory in Social Media Data Clustering: Roles, Methodologies, and Applications

Description

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 analytics Clustering as a fundamental technique for unsupervised knowledge discovery and data mining A 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 domain Adaptive 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? . Front Matter ....Pages i-xv Front Matter ....Pages 1-1 Introduction (Lei Meng, Ah-Hwee Tan, Donald C. Wunsch II)....Pages 3-14 Clustering and Its Extensions in the Social Media Domain (Lei Meng, Ah-Hwee Tan, Donald C. Wunsch II)....Pages 15-44 Adaptive Resonance Theory (ART) for Social Media Analytics (Lei Meng, Ah-Hwee Tan, Donald C. Wunsch II)....Pages 45-89 Front Matter ....Pages 91-91 Personalized Web Image Organization (Lei Meng, Ah-Hwee Tan, Donald C. Wunsch II)....Pages 93-110 Socially-Enriched Multimedia Data Co-clustering (Lei Meng, Ah-Hwee Tan, Donald C. Wunsch II)....Pages 111-135 Community Discovery in Heterogeneous Social Networks (Lei Meng, Ah-Hwee Tan, Donald C. Wunsch II)....Pages 137-154 Online Multimodal Co-indexing and Retrieval of Social Media Data (Lei Meng, Ah-Hwee Tan, Donald C. Wunsch II)....Pages 155-174 Concluding Remarks (Lei Meng, Ah-Hwee Tan, Donald C. Wunsch II)....Pages 175-179 Back Matter ....Pages 181-190