Publication Type

Book Chapter

Version

publishedVersion

Publication Date

5-2019

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.

Discipline

Databases and Information Systems | Software Engineering

Research Areas

Data Science and Engineering

Publication

Adaptive Resonance Theory in Social Media Data Clustering

Editor

MENG, Lei; TAN, Ah-Hwee; WUNSCH, Donald C.

First Page

15

Last Page

44

ISBN

9783030029845

Identifier

10.1007/978-3-030-02985-2_2

Publisher

Springer

City or Country

Cham

Additional URL

https://doi.org/10.1007/978-3-030-02985-2_2

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