Fundamentals of Data Science: Theory and Practice presents basic and advanced concepts in data science along with real-life applications. The book provides students, researchers and professionals at different levels a good understanding of the concepts of data science, machine learning, data mining and analytics. Users will find the authors’ research experiences and achievements in data science applications, along with in-depth discussions on topics that are essential for data science projects, including pre-processing, that is carried out before applying predictive and descriptive data analysis tasks and proximity measures for numeric, categorical and mixed-type data. The book's authors include a systematic presentation of many predictive and descriptive learning algorithms, including recent developments that have successfully handled large datasets with high accuracy. In addition, a number of descriptive learning tasks are included.

Biological Network Analysis: Trends, Approaches, Graph Theory, and Algorithms considers three major biological networks, including Gene Regulatory Networks (GRN), Protein-Protein Interaction Networks (PPIN), and Human Brain Connectomes. The book's authors discuss various graph theoretic and data analytics approaches used to analyze these networks with respect to available tools, technologies, standards, algorithms and databases for generating, representing and analyzing graphical data. As a wide variety of algorithms have been developed to analyze and compare networks, this book is a timely resource.

This monograph presents a study on data mining techniques and its application in medical image analysis for cancer detection. An extensive survey and comparative study on various clustering and association mining techniques has been presented. Monograph also reported one new density based clustering technique EnDBSCAN that can detect embedded cluster structure and OPAM- an efficient one pass technique for mining frequent item sets. Results have been reported to establish the effectiveness of the works in light of various synthetic datasets. Finally it discusses about medical imaging and potentials of data mining techniques for analyzing medical imagery for detecting disease like cancer. As a case study it discusses two cancer type namely brain and skin cancer. It also proposes architecture for automatic brain cancer detection tool based on Data Mining techniques.