Clustering and its extensions in the social media domain

• Main Authors: MENG, Lei, TAN, Ah-hwee, WUNSCH, Donald C.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2019
• Subjects: Databases and Information Systems; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/6061; https://ink.library.smu.edu.sg/context/sis_research/article/7064/viewcontent/clustering.pdf
• Institution: Singapore Management University

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.