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Books > Computing & IT > Applications of computing > Databases > Data mining
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Pattern Mining with Evolutionary Algorithms (Hardcover, 1st ed. 2016)
Loot Price: R3,415
Discovery Miles 34 150
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Pattern Mining with Evolutionary Algorithms (Hardcover, 1st ed. 2016)
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This book provides a comprehensive overview of the field of pattern
mining with evolutionary algorithms. To do so, it covers formal
definitions about patterns, patterns mining, type of patterns and
the usefulness of patterns in the knowledge discovery process. As
it is described within the book, the discovery process suffers from
both high runtime and memory requirements, especially when high
dimensional datasets are analyzed. To solve this issue, many
pruning strategies have been developed. Nevertheless, with the
growing interest in the storage of information, more and more
datasets comprise such a dimensionality that the discovery of
interesting patterns becomes a challenging process. In this regard,
the use of evolutionary algorithms for mining pattern enables the
computation capacity to be reduced, providing sufficiently good
solutions. This book offers a survey on evolutionary computation
with particular emphasis on genetic algorithms and genetic
programming. Also included is an analysis of the set of quality
measures most widely used in the field of pattern mining with
evolutionary algorithms. This book serves as a review of the most
important evolutionary algorithms for pattern mining. It considers
the analysis of different algorithms for mining different type of
patterns and relationships between patterns, such as frequent
patterns, infrequent patterns, patterns defined in a continuous
domain, or even positive and negative patterns. A completely new
problem in the pattern mining field, mining of exceptional
relationships between patterns, is discussed. In this problem the
goal is to identify patterns which distribution is exceptionally
different from the distribution in the complete set of data
records. Finally, the book deals with the subgroup discovery task,
a method to identify a subgroup of interesting patterns that is
related to a dependent variable or target attribute. This subgroup
of patterns satisfies two essential conditions: interpretability
and interestingness.
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