This book highlights the state of the art and recent advances in Big Data clustering methods
and their innovative applications in contemporary AI-driven systems. The book chapters discuss
Deep Learning for Clustering Blockchain data clustering Cybersecurity applications such as
insider threat detection scalable distributed clustering methods for massive volumes of data
clustering Big Data Streams such as streams generated by the confluence of Internet of Things
digital and mobile health human-robot interaction and social networks Spark-based Big Data
clustering using Particle Swarm Optimization and Tensor-based clustering for Web graphs
sensor streams and social networks. The chapters in the book include a balanced coverage of
big data clustering theory methods tools frameworks applications representation
visualization and clustering validation.
