Authors:
- Deepens your understanding on social media analytics
- Broadens your insight on clustering as a fundamental technique for unsupervised knowledge discovery and data mining
- Equips readers with a class of neural inspired algorithms based on adaptive resonance theory (ART), to tackle challenges in clustering big social media data
- Offers a step-by-step guide to developing unsupervised machine learning algorithms for real-world applications that transfer social media data to actionable intelligence
Part of the book series: Advanced Information and Knowledge Processing (AI&KP)
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Table of contents (8 chapters)
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Front Matter
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Back Matter
About this book
Social media data contains our communication and online sharing, mirroring our daily life. This book looks at how we can use and what we can discover from such big data:
- Basic knowledge (data & challenges) on social media analytics
- Clustering as a fundamental technique for unsupervised knowledge discovery and data mining
- A class of neural inspired algorithms, based on adaptive resonance theory (ART), tackling challenges in big social media data clustering
- Step-by-step practices of developing unsupervised machine learning algorithms for real-world applications in social media domain
Adaptive Resonance Theory in Social Media Data Clustering stands on the fundamental breakthrough in cognitive and neural theory, i.e. adaptive resonance theory, which simulates how a brain processes information to perform memory, learning, recognition, and prediction.
It presents initiatives on the mathematical demonstration of ART’s learning mechanisms in clustering, and illustrates how to extend the base ART model to handle the complexity and characteristics of social media data and perform associative analytical tasks.
Both cutting-edge research and real-world practices on machine learning and social media analytics are included in the book and if you wish to learn the answers to the following questions, this book is for you:
- How to process big streams of multimedia data?
- How to analyze social networks with heterogeneous data?
- How to understand a user’s interests by learning from online posts and behaviors?
- How to create a personalized search engine by automatically indexing and searching multimodal information resources?
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Authors and Affiliations
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NTU-UBC Research Center of Excellence in Active Living for the Elderly (LILY), Nanyang Technological University, Singapore, Singapore
Lei Meng
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School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
Ah-Hwee Tan
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Applied Computational Intelligence Laboratory, Missouri University of Science and Technology, Rolla, USA
Donald C. Wunsch II
Bibliographic Information
Book Title: Adaptive Resonance Theory in Social Media Data Clustering
Book Subtitle: Roles, Methodologies, and Applications
Authors: Lei Meng, Ah-Hwee Tan, Donald C. Wunsch II
Series Title: Advanced Information and Knowledge Processing
DOI: https://doi.org/10.1007/978-3-030-02985-2
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Hardcover ISBN: 978-3-030-02984-5Published: 14 May 2019
eBook ISBN: 978-3-030-02985-2Published: 30 April 2019
Series ISSN: 1610-3947
Series E-ISSN: 2197-8441
Edition Number: 1
Number of Pages: XV, 190
Number of Illustrations: 19 b/w illustrations, 34 illustrations in colour
Topics: Data Mining and Knowledge Discovery, Algorithm Analysis and Problem Complexity, Cognitive Psychology, Pattern Recognition