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.
