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A Fruitful Field for Researching Data Mining Methodology and for Solving Real-Life ProblemsContrast 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 ApplicationsIn 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.
I Preliminaries and Statistical Contrast Measures
1 Preliminaries
2 Statistical Measures for Contrast Patterns
II Contrast Mining Algorithms
3 Mining Emerging Patterns Using Tree Structures or Tree Based Searches
4 Mining Emerging Patterns Using Zero-Suppressed Binary Decision Diagrams
5 Efficient Direct Mining of Selective Discriminative Patterns for Classification
6 Mining Emerging Patterns from Structured Data
7 Incremental Maintenance of Emerging Patterns
III Generalized Contrasts, Emerging Data Cubes, and Rough Sets
8 More Expressive Contrast Patterns and Their Mining
9 Emerging Data Cube Representations for OLAP Database Mining
10 Relation Between Jumping Emerging Patterns and Rough Set Theory
IV Contrast Mining for Classification & Clustering
11 Overview and Analysis of Contrast Pattern Based Classification
12 Using Emerging Patterns in Outlier and Rare-Class Prediction
13 Enhancing Traditional Classifiers Using Emerging Patterns
14 CPC: A Contrast Pattern Based Clustering Algorithm
V Contrast Mining for Bioinformatics and Chemoinformatics
15 Emerging Pattern Based Rules Characterizing Subtypes of Leukemia
16 Discriminating Gene Transfer and Microarray Concordance Analysis
17 Towards Mining Optimal Emerging Patterns Amidst 1000s of Genes
18 Emerging Chemical Patterns – Theory and Applications
19 Emerging Patterns as Structural Alerts for Computational Toxicology
VI Contrast Mining for Special Domains
20 Emerging Patterns and Classification for Spatial and Image Data
21 Geospatial Contrast Mining with Applications on Labeled Spatial Data
22 Mining Emerging Patterns for Activity Recognition
23 Emerging Pattern Based Prediction of Heart Diseases and Powerline Safety*
24 Emerging Pattern Based Crime Spots Analysis and Rental Price Prediction
VII Survey of Other Papers
25 Overview of Results on Contrast Mining and Applications
Bibliography
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