Contrast Data Mining: Concepts, Algorithms, and Applications

Guozhu Dong, James Bailey

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September 7, 2012 by Chapman and Hall/CRC
Reference - 434 Pages - 78 B/W Illustrations
ISBN 9781439854327 - CAT# K12517
Series: Chapman & Hall/CRC Data Mining and Knowledge Discovery Series

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Features

  • Provides the first comprehensive book on contrast mining and applications
  • Presents contrast mining algorithms and measures on contrast patterns
  • Covers contrast pattern-based classification, clustering, outlier detection, and enhancement of traditional classifiers
  • Describes applications of contrast mining in bioinformatics and chemoinformatics, including an importance index of genes based on their interactions
  • Includes applications of contrast mining for the analysis of images, sequences, graphs, texts, geospatial data, diseases, activity recognition, crime locations, and power line safety

Summary

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.