Explores how granular computing plays a significant role in advancing
machine learning towards in-depth processing of big data
Introduces the main characteristics of big data, i.e. the five
Vs--Volume, Velocity, Variety, Veracity, and Variability
Presents popular types of traditional machine learning in terms of their
key features and limitations in the context of big data
Discusses the need for and different uses of granular computing based
machine learning
Presents several case studies of big data by using biomedical data and
sentiment data, demonstrating recent advances
Stresses the theoretical significance, practical importance,
methodological impact, and philosophical aspects