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Adaptive Resonance Theory (ART) for Social Media Analytics

  • Lei MengEmail author
  • Ah-Hwee Tan
  • Donald C. Wunsch II
Chapter
Part of the Advanced Information and Knowledge Processing book series (AI&KP)

Abstract

This chapter presents the ART-based clustering algorithms for social media analytics in detail. Sections 3.1 and 3.2 introduce Fuzzy ART and its clustering mechanisms, respectively, which provides a deep understanding of the base model that is used and extended for handling the social media clustering challenges. Important concepts such as vigilance region (VR) and its properties are explained and proven. Subsequently, Sects. 3.33.7 illustrate five types of ART variants, each of which addresses the challenges in one social media analytical scenario, including automated parameter adaptation, user preference incorporation, short text clustering, heterogeneous data co-clustering and online streaming data indexing. The content of this chapter is several prior studies, including Probabilistic ART [15] (©2012 IEEE. Reprinted, with permission, from [15]), Generalized Heterogeneous Fusion ART [20] (©2014 IEEE. Reprinted, with permission, from [20]), Vigilance Adaptation ART [19] (©2016 IEEE. Reprinted, with permission, from [19]), and Online Multimodal Co-indexing ART [17] (http://dx.doi.org/10.1145/2671188.2749362).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.NTU-UBC Research Center of Excellence in Active Living for the Elderly (LILY)Nanyang Technological UniversitySingaporeSingapore
  2. 2.School of Computer Science and EngineeringNanyang Technological UniversitySingaporeSingapore
  3. 3.Applied Computational Intelligence LaboratoryMissouri University of Science and TechnologyRollaUSA

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