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Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
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Periodic Pattern Mining - Theory, Algorithms, and Applications (Paperback, 1st ed. 2021)
Loot Price: R4,478
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Periodic Pattern Mining - Theory, Algorithms, and Applications (Paperback, 1st ed. 2021)
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This book provides an introduction to the field of periodic pattern
mining, reviews state-of-the-art techniques, discusses recent
advances, and reviews open-source software. Periodic pattern mining
is a popular and emerging research area in the field of data
mining. It involves discovering all regularly occurring patterns in
temporal databases. One of the major applications of periodic
pattern mining is the analysis of customer transaction databases to
discover sets of items that have been regularly purchased by
customers. Discovering such patterns has several implications for
understanding the behavior of customers. Since the first work on
periodic pattern mining, numerous studies have been published and
great advances have been made in this field. The book consists of
three main parts: introduction, algorithms, and applications. The
first chapter is an introduction to pattern mining and periodic
pattern mining. The concepts of periodicity, periodic support,
search space exploration techniques, and pruning strategies are
discussed. The main types of algorithms are also presented such as
periodic-frequent pattern growth, partial periodic pattern-growth,
and periodic high-utility itemset mining algorithm. Challenges and
research opportunities are reviewed. The chapters that follow
present state-of-the-art techniques for discovering periodic
patterns in (1) transactional databases, (2) temporal databases,
(3) quantitative temporal databases, and (4) big data. Then, the
theory on concise representations of periodic patterns is
presented, as well as hiding sensitive information using
privacy-preserving data mining techniques. The book concludes with
several applications of periodic pattern mining, including
applications in air pollution data analytics, accident data
analytics, and traffic congestion analytics.
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