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Books > Computing & IT > General theory of computing
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Data Cleaning (Paperback)
Loot Price: R1,763
Discovery Miles 17 630
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Data Cleaning (Paperback)
Series: ACM Collection II
Expected to ship within 18 - 22 working days
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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, we give an overview of the end-to-end data cleaning
process, describing various error detection and repair methods, and
attempt to anchor these proposals with multiple taxonomies and
views. Specifically, we cover 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, we
include 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.
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