Adaptive Resonance Theory (ART) for Social Media Analytics
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 adaptive resonance theory 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]
Recommended Citation
L. Meng et al., "Adaptive Resonance Theory (ART) for Social Media Analytics," Advanced Information and Knowledge Processing, pp. 45 - 89, Springer London, May 2019.
The definitive version is available at https://doi.org/10.1007/978-3-030-02985-2_3
Department(s)
Electrical and Computer Engineering
Research Center/Lab(s)
Center for High Performance Computing Research
International Standard Serial Number (ISSN)
1610-3947
Document Type
Book - Chapter
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2019 Springer London, All rights reserved.
Publication Date
01 May 2019
