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Books > Computing & IT > General theory of computing
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Data Cleaning (Hardcover)
Loot Price: R2,553
Discovery Miles 25 530
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Data Cleaning (Hardcover)
Series: ACM Collection II
Expected to ship within 10 - 15 working days
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This is an overview of the end-to-end data cleaning process. Data
quality is one of the most important problems in data management,
since dirty data often leads to inaccurate data analytics results
and incorrect business decisions. Poor data across businesses and
the U.S. government are reported to cost trillions of dollars a
year. Multiple surveys show that dirty data is the most common
barrier faced by data scientists. Not surprisingly, developing
effective and efficient data cleaning solutions is challenging and
is rife with deep theoretical and engineering problems. This book
is about data cleaning, which is used to refer to all kinds of
tasks and activities to detect and repair errors in the data.
Rather than focus on a particular data cleaning task, this book
describes various error detection and repair methods, and attempts
to anchor these proposals with multiple taxonomies and views.
Specifically, it covers four of the most common and important data
cleaning tasks, namely, outlier detection, data transformation,
error repair (including imputing missing values), and data
deduplication. Furthermore, due to the increasing popularity and
applicability of machine learning techniques, it includes a chapter
that specifically explores how machine learning techniques are used
for data cleaning, and how data cleaning is used to improve machine
learning models. This book is intended to serve as a useful
reference for researchers and practitioners who are interested in
the area of data quality and data cleaning. It can also be used as
a textbook for a graduate course. Although we aim at covering
state-of-the-art algorithms and techniques, we recognize that data
cleaning is still an active field of research and therefore provide
future directions of research whenever appropriate.
General
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