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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.
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