Adaptive Resonance Theory in Social Media Data Clustering : Roles, Methodologies, and Applications / by Lei Meng, Ah-Hwee Tan, Donald C. Wunsch II.

Format:

Book

Author/Creator:

Meng, Lei, author.

Tan, Ah-Hwee, author.

Wunsch II, Donald C., author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Advanced information and knowledge processing 1610-3947

Advanced Information and Knowledge Processing, 1610-3947

Language:

English

Subjects (All):

Data mining.

Algorithms.

Cognitive psychology.

Pattern perception.

Data Mining and Knowledge Discovery.

Algorithm Analysis and Problem Complexity.

Cognitive Psychology.

Pattern Recognition.

Local Subjects:

Data Mining and Knowledge Discovery.

Algorithm Analysis and Problem Complexity.

Cognitive Psychology.

Pattern Recognition.

Physical Description:

1 online resource (XV, 190 pages) : 53 illustrations, 34 illustrations in color.

Edition:

First edition 2019.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2019.

System Details:

text file PDF

Summary:

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 and 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, id est 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?

Contents:

Part 1: Theories

Introduction

Clustering and Extensions in the Social Media Domain

Adaptive Resonance Theory (ART) for Social Media Analytics

Part II: Applications

Personalized Web Image Organization

Socially-Enriched Multimedia Data Co-Clustering

Community Discovery in Heterogeneous Social Networks

Online Multimodal Co-Indexing and Retrieval of Social Media Data

Concluding Remarks.

Other Format:

Printed edition:

ISBN:

978-3-030-02985-2

9783030029852

9783030029845

9783030029869

Access Restriction:

Restricted for use by site license.