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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