Publication Type
Book Chapter
Version
publishedVersion
Publication Date
5-2019
Abstract
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).
Discipline
Databases and Information Systems | Social Media
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
45
Last Page
89
ISBN
9783030029845
Identifier
10.1007/978-3-030-02985-2_3
Publisher
Springer
City or Country
Cham
Citation
MENG, Lei; TAN, Ah-Hwee; and WUNSCH, Donald C. II.
Adaptive Resonance Theory (ART) for social media analytics. (2019). Adaptive Resonance Theory in Social Media Data Clustering. 45-89.
Available at: https://ink.library.smu.edu.sg/sis_research/5384
Copyright Owner and License
Publisher
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1007/978-3-030-02985-2_3