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Books > Computing & IT > Applications of computing > Databases > Data mining
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Exploiting the Power of Group Differences - Using Patterns to Solve Data Analysis Problems (Paperback)
Loot Price: R1,759
Discovery Miles 17 590
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Exploiting the Power of Group Differences - Using Patterns to Solve Data Analysis Problems (Paperback)
Series: Synthesis Lectures on Data Mining and Knowledge Discovery
Expected to ship within 10 - 15 working days
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This book presents pattern-based problem-solving methods for a
variety of machine learning and data analysis problems. The methods
are all based on techniques that exploit the power of group
differences. They make use of group differences represented using
emerging patterns (aka contrast patterns), which are patterns that
match significantly different numbers of instances in different
data groups. A large number of applications outside of the
computing discipline are also included. Emerging patterns (EPs) are
useful in many ways. EPs can be used as features, as simple
classifiers, as subpopulation signatures/characterizations, and as
triggering conditions for alerts. EPs can be used in gene ranking
for complex diseases since they capture multi-factor interactions.
The length of EPs can be used to detect anomalies, outliers, and
novelties. Emerging/contrast pattern based methods for clustering
analysis and outlier detection do not need distance metrics,
avoiding pitfalls of the latter in exploratory analysis of high
dimensional data. EP-based classifiers can achieve good accuracy
even when the training datasets are tiny, making them useful for
exploratory compound selection in drug design. EPs can serve as
opportunities in opportunity-focused boosting and are useful for
constructing powerful conditional ensembles. EP-based methods often
produce interpretable models and results. In general, EPs are
useful for classification, clustering, outlier detection, gene
ranking for complex diseases, prediction model analysis and
improvement, and so on. EPs are useful for many tasks because they
represent group differences, which have extraordinary power.
Moreover, EPs represent multi-factor interactions, whose effective
handling is of vital importance and is a major challenge in many
disciplines. Based on the results presented in this book, one can
clearly say that patterns are useful, especially when they are
linked to issues of interest. We believe that many effective ways
to exploit group differences' power still remain to be discovered.
Hopefully this book will inspire readers to discover such new ways,
besides showing them existing ways, to solve various challenging
problems.
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