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.
