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? .
