The last decade has witnessed how social media in the era of Web 2.0 reshapes the way people communicate, interact, and entertain in daily life and incubates the prosperity of various user-centric platforms, such as social networking, question answering, massive open online courses (MOOC), and e-commerce platforms. The available rich user-generated multimedia data on the web has evolved traditional ways of understanding multimedia research and has led to numerous emerging topics on human-centric analytics and services, such as user profiling, social network mining, crowd behavior analysis, and personalized recommendation. Clustering, as an important tool for mining information groups and in-group shared characteristics, has been widely investigated for the knowledge discovery and data mining tasks in social media analytics. Whereas, social media data has numerous characteristics that raise challenges for traditional clustering techniques, such as the massive amount, diverse content, heterogeneous media sources, noisy user-generated content, and the generation in stream manner. This leads to the scenario where the clustering algorithms used in the literature of social media applications are usually variants of a few traditional algorithms, such as K-means, non-negative matrix factorization (NMF), and graph clustering. Developing a fast and robust clustering algorithm for social media analytics is still an open problem. This chapter will give a bird’s eye view of clustering in social media analytics, in terms of data characteristics, challenges and issues, and a class of novel approaches based on adaptive resonance theory (ART).
