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2019 | OriginalPaper | Chapter

2. Clustering and Its Extensions in the Social Media Domain

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 summarizes existing clustering and related approaches for the identified challenges as described in Sect. 1.​2 and presents the key branches of social media mining applications where clustering holds a potential. Specifically, several important types of clustering algorithms are first illustrated, including clustering, semi-supervised clustering, heterogeneous data co-clustering, and online clustering. Subsequently, Sect. 2.5 presents a review on existing techniques that help decide the value of the predefined number of clusters (required by most clustering algorithms) automatically and highlights the clustering algorithms that do not require such a parameter. It better illustrates the challenge of input parameter sensitivity of clustering algorithms when applied to large and complex social media data. Furthermore, in Sect. 2.6, a survey on several main applications of clustering algorithms to social media mining tasks is offered, including web image organization, multi-modal information fusion, user community detection, user sentiment analysis, social event detection, community question answering, social media data indexing and retrieval, and recommender systems in social networks.

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next chapter Adaptive Resonance Theory (ART) for Social Media Analytics
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Metadata
Title
Clustering and Its Extensions in the Social Media Domain
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_2

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