Clustering Methods for Big Data Analytics

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.

Introduction. - Clustering large scale data. - Clustering heterogeneous data. - Distributed clustering methods. - Clustering structured and unstructured data. - Clustering and unsupervised learning for deep learning. - Deep learning methods for clustering. - Clustering high speed cloud, grid, and streaming data. - Extension of partitioning, model based, density based, grid based, fuzzy and evolutionary clustering methods for big data analysis. - Large documents and textual data clustering. - Applications of big data clustering methods. - Clustering multimedia and multi-structured data. - Large-scale recommendation systems and social media systems. - Clustering multimedia and multi-structured data. - Real life applications of big data clustering. - Validation measures for big data clustering methods. - Conclusion.