This book provides the reader with a basic understanding of the formal concepts of cluster, clustering, partition, cluster analysis etc.The book explains feature based, graph-based and spectral clustering methods and discusses their formal similarities and differences.

This book describes computational problems related to kernel density estimation (KDE)-one of the most important and widely used data smoothing techniques. This book is an attempt to remedy this. The book primarily addresses researchers and advanced graduate or postgraduate students who are interested in Kand its computational aspects.

The edited volume deals with different contours of data science with special reference to data management for the research innovation landscape.

This book presents the current trends, technologies, and challenges in Big Data in the diversified field of engineering and sciences.

Further, the book shows new directions for cooperation between science and industry and facilitates efficient transfer of knowledge in the area of intelligent information systems.