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

• Main Authors: Meng, Lei (Author, http://id.loc.gov/vocabulary/relators/aut), Tan, Ah-Hwee (http://id.loc.gov/vocabulary/relators/aut), Wunsch II, Donald C. (http://id.loc.gov/vocabulary/relators/aut)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: Cham : Springer International Publishing : Imprint: Springer, 2019.
• Edition: 1st ed. 2019.
• Series: Advanced Information and Knowledge Processing,
• Subjects: Data mining.; Algorithms.; Cognitive psychology.; Pattern recognition.; Data Mining and Knowledge Discovery.; Algorithm Analysis and Problem Complexity.; Cognitive Psychology.; Pattern Recognition.

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...

Table of Contents:

• Part 1: Theories
• Introduction
• Clustering and Extensions in the Social Media Domain
• Adaptive Resonance Theory (ART) for Social Media Analytics
• Part II: Applications
• Personalized Web Image Organization
• Socially-Enriched Multimedia Data Co-Clustering
• Community Discovery in Heterogeneous Social Networks
• Online Multimodal Co-Indexing and Retrieval of Social Media Data
• Concluding Remarks.