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Data Preprocessing for Data Mining addresses one of the most
important issues within the well-known Knowledge Discovery from
Data process. Data directly taken from the source will likely have
inconsistencies, errors or most importantly, it is not ready to be
considered for a data mining process. Furthermore, the increasing
amount of data in recent science, industry and business
applications, calls to the requirement of more complex tools to
analyze it. Thanks to data preprocessing, it is possible to convert
the impossible into possible, adapting the data to fulfill the
input demands of each data mining algorithm. Data preprocessing
includes the data reduction techniques, which aim at reducing the
complexity of the data, detecting or removing irrelevant and noisy
elements from the data. This book is intended to review the tasks
that fill the gap between the data acquisition from the source and
the data mining process. A comprehensive look from a practical
point of view, including basic concepts and surveying the
techniques proposed in the specialized literature, is given.Each
chapter is a stand-alone guide to a particular data preprocessing
topic, from basic concepts and detailed descriptions of classical
algorithms, to an incursion of an exhaustive catalog of recent
developments. The in-depth technical descriptions make this book
suitable for technical professionals, researchers, senior
undergraduate and graduate students in data science, computer
science and engineering.
This book offers a comprehensible overview of Big Data
Preprocessing, which includes a formal description of each problem.
It also focuses on the most relevant proposed solutions. This book
illustrates actual implementations of algorithms that helps the
reader deal with these problems. This book stresses the gap that
exists between big, raw data and the requirements of quality data
that businesses are demanding. This is called Smart Data, and to
achieve Smart Data the preprocessing is a key step, where the
imperfections, integration tasks and other processes are carried
out to eliminate superfluous information. The authors present the
concept of Smart Data through data preprocessing in Big Data
scenarios and connect it with the emerging paradigms of IoT and
edge computing, where the end points generate Smart Data without
completely relying on the cloud. Finally, this book provides some
novel areas of study that are gathering a deeper attention on the
Big Data preprocessing. Specifically, it considers the relation
with Deep Learning (as of a technique that also relies in large
volumes of data), the difficulty of finding the appropriate
selection and concatenation of preprocessing techniques applied and
some other open problems. Practitioners and data scientists who
work in this field, and want to introduce themselves to
preprocessing in large data volume scenarios will want to purchase
this book. Researchers that work in this field, who want to know
which algorithms are currently implemented to help their
investigations, may also be interested in this book.
This book offers a comprehensible overview of Big Data
Preprocessing, which includes a formal description of each problem.
It also focuses on the most relevant proposed solutions. This book
illustrates actual implementations of algorithms that helps the
reader deal with these problems. This book stresses the gap that
exists between big, raw data and the requirements of quality data
that businesses are demanding. This is called Smart Data, and to
achieve Smart Data the preprocessing is a key step, where the
imperfections, integration tasks and other processes are carried
out to eliminate superfluous information. The authors present the
concept of Smart Data through data preprocessing in Big Data
scenarios and connect it with the emerging paradigms of IoT and
edge computing, where the end points generate Smart Data without
completely relying on the cloud. Finally, this book provides some
novel areas of study that are gathering a deeper attention on the
Big Data preprocessing. Specifically, it considers the relation
with Deep Learning (as of a technique that also relies in large
volumes of data), the difficulty of finding the appropriate
selection and concatenation of preprocessing techniques applied and
some other open problems. Practitioners and data scientists who
work in this field, and want to introduce themselves to
preprocessing in large data volume scenarios will want to purchase
this book. Researchers that work in this field, who want to know
which algorithms are currently implemented to help their
investigations, may also be interested in this book.
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