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

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...

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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)
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.
Online Access:https://doi.org/10.1007/978-3-030-02985-2
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.

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