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With the growing interest in and use of big data analytics in many
industries and in many research fields around the globe, this new
volume addresses the need for a comprehensive resource on the core
concepts of big data analytics along with the tools, techniques,
and methodologies. The book gives the why and the how of big data
analytics in an organized and straightforward manner, using both
theoretical and practical approaches. The book's authors have
organized the contents in a systematic manner, starting with an
introduction and overview of big data analytics and then delving
into pre-processing methods, feature selection methods and
algorithms, big data streams, and big data classification. Such
terms and methods as swarm intelligence, data mining, the bat
algorithm and genetic algorithms, big data streams, and many more
are discussed. The authors explain how deep learning and machine
learning along with other methods and tools are applied in big data
analytics. The last section of the book presents a selection of
illustrative case studies that show examples of the use of data
analytics in industries such as health care, business, education,
and social media. Research Practitioner's Handbook on Big Data
Analytics will be a valuable addition to the libraries of
practitioners in data collection in many industries along with
research scholars and faculty in the domain of big data analytics.
The book can also serve as a handy textbook for courses in data
collection, data mining, and big data analytics.
At the highest level of description, this book is about
dimensionality reduction for data mining. It focuses on dimensional
reduction on Small, Medium and very large Scale data. This book is
not intended as an introduction to Feature selection, although we
do provide the background necessary in several areas in order to
facilitate the reader's comprehension of their respective roles in
data mining. This book reviews state-of-the-art dimensionality
reduction techniques for analyzing enormous quantities of raw data
in high-dimensional data spaces to extract new information useful
to the decision-making process.
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