Advanced Information and Knowledge Processing

Adaptive Resonance Theory in Social Media Data Clustering

Roles, Methodologies, and Applications

Authors: Meng, Lei, Tan, Ah-Hwee, Wunsch, Donald

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  • 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
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eBook 85,59 €
price for Spain (gross)
  • ISBN 978-3-030-02985-2
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover 103,99 €
price for Spain (gross)
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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Table of contents (8 chapters)

Table of contents (8 chapters)

Buy this book

eBook 85,59 €
price for Spain (gross)
  • ISBN 978-3-030-02985-2
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover 103,99 €
price for Spain (gross)
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Bibliographic Information

Bibliographic Information
Book Title
Adaptive Resonance Theory in Social Media Data Clustering
Book Subtitle
Roles, Methodologies, and Applications
Authors
Series Title
Advanced Information and Knowledge Processing
Copyright
2019
Publisher
Springer International Publishing
Copyright Holder
Springer Nature Switzerland AG
eBook ISBN
978-3-030-02985-2
DOI
10.1007/978-3-030-02985-2
Hardcover ISBN
978-3-030-02984-5
Series ISSN
1610-3947
Edition Number
1
Number of Pages
XV, 190
Number of Illustrations
19 b/w illustrations, 34 illustrations in colour
Topics