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2019 | OriginalPaper | Chapter

3. Adaptive Resonance Theory (ART) for Social Media Analytics

Authors : Lei Meng, Ah-Hwee Tan, Donald C. Wunsch II

Published in: Adaptive Resonance Theory in Social Media Data Clustering

Publisher: Springer International Publishing

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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. 3.3–3.7 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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previous chapter Clustering and Its Extensions in the Social Media Domain
next chapter Personalized Web Image Organization
Footnotes
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Metadata
Title
Adaptive Resonance Theory (ART) for Social Media Analytics
Authors
Lei Meng
Ah-Hwee Tan
Donald C. Wunsch II
Copyright Year
2019
Book
Adaptive Resonance Theory in Social Media Data Clustering

Print ISBN: 978-3-030-02984-5

Electronic ISBN: 978-3-030-02985-2

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-02985-2_3

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