A Fruitful Field for Researching Data Mining Methodology and for
Solving Real-Life Problems
Contrast Data Mining: Concepts, Algorithms, and Applications
collects recent results from this specialized area of data mining
that have previously been scattered in the literature, making them
more accessible to researchers and developers in data mining and
other fields. The book not only presents concepts and techniques
for contrast data mining, but also explores the use of contrast
mining to solve challenging problems in various scientific,
medical, and business domains.
Learn from Real Case Studies of Contrast Mining
Applications
In this volume, researchers from around the world specializing in
architecture engineering, bioinformatics, computer science,
medicine, and systems engineering focus on the mining and use of
contrast patterns. They demonstrate many useful and powerful
capabilities of a variety of contrast mining techniques and
algorithms, including tree-based structures, zero-suppressed binary
decision diagrams, data cube representations, and clustering
algorithms. They also examine how contrast mining is used in
leukemia characterization, discriminative gene transfer and
microarray analysis, computational toxicology, spatial and image
data classification, voting analysis, heart disease prediction,
crime analysis, understanding customer behavior, genetic
algorithms, and network security.
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