Publication Type
Book Chapter
Version
publishedVersion
Publication Date
5-2019
Abstract
This chapter summarizes the major contributions in this book and discusses their possible positions and requirements in some future scenarios. Section 8.1 follows the book structure to revisit the key contributions of this book in both theories and applications. The developed algorithms, such as the VA-ARTs for hyperparameter adaptation and the GHF-ART for multimedia representation and fusion, and the four applications, such as clustering and retrieving socially enriched multimedia data, are concentrated using one paragraph and three paragraphs, respectively. In Sect. 8.2, the roles of the proposed ART-embodied algorithms in social media clustering tasks are highlighted, and their possible evolutions using the state-of-the-art representation learning techniques to fit the increasingly rich social media data and demands are discussed.
Discipline
Databases and Information Systems | Software Engineering
Research Areas
Data Science and Engineering
Publication
Adaptive Resonance Theory in Social Media Data Clustering
First Page
175
Last Page
179
ISBN
9783030029845
Identifier
10.1007/978-3-030-02985-2_8
Publisher
Springer
City or Country
Cham
Citation
MENG, Lei; TAN, Ah-hwee; and WUNSCH, Donald C..
Concluding remarks. (2019). Adaptive Resonance Theory in Social Media Data Clustering. 175-179.
Available at: https://ink.library.smu.edu.sg/sis_research/6062
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_8