Contrast Data Mining Concepts, Algorithms, and Applications

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.

Preliminaries and Statistical Contrast MeasuresPreliminaries, Guozhu DongStatistical Measures for Contrast Patterns, James Bailey Contrast Mining AlgorithmsMining Emerging Patterns Using Tree Structures or Tree-Based Searches, James Bailey and Kotagiri RamamohanaraoMining Emerging Patterns Using Zero-Suppressed Binary Decision Diagrams, James Bailey and Elsa LoekitoEfficient Direct Mining of Selective Discriminative Patterns for Classification, Hong Cheng, Jiawei Han, Xifeng Yan, and Philip S. YuMining Emerging Patterns from Structured Data, James BaileyIncremental Maintenance of Emerging Patterns, Mengling Feng and Guozhu Dong Generalized Contrasts, Emerging Data Cubes, and Rough SetsMore Expressive Contrast Patterns and Their Mining, Lei Duan, Milton Garcia Borroto, and Guozhu DongEmerging Data Cube Representations for OLAP Database Mining, Sébastien Nedjar, Lotfi Lakhal, and Rosine CicchettiRelation between Jumping Emerging Patterns and Rough Set Theory, Pawel Terlecki and Krzysztof Walczak Contrast Mining for Classification and ClusteringOverview and Analysis of Contrast Pattern-Based Classification, Xiuzhen Zhang and Guozhu DongUsing Emerging Patterns in Outlier and Rare-Class Prediction, Lijun Chen and Guozhu DongEnhancing Traditional Classifiers Using Emerging Patterns, Guozhu Dong and Kotagiri RamamohanaraoCPC: A Contrast Pattern-Based Clustering Algorithm, Neil Fore and Guozhu Dong Contrast Mining for Bioinformatics and ChemoinformaticsEmerging Pattern-Based Rules Characterizing Subtypes of Leukemia, Jinyan Li and Limsoon WongDiscriminating Gene Transfer and Microarray Concordance Analysis, Shihong Mao and Guozhu DongToward Mining Optimal Emerging Patterns amid 1000s of Genes, Shihong Mao and Guozhu DongEmerging Chemical Patterns — Theory and Applications, Jens Auer, Martin Vogt, and Jürgen BajorathEmerging Patterns as Structural Alerts for Computational Toxicology, Bertrand Cuissart, Guillaume Poezevara, Bruno Crémilleux, Alban Lepailleur, and Ronan Bureau Contrast Mining for Special DomainsEmerging Patterns and Classification for Spatial and Image Data, Lukasz Kobylinski and Krzysztof Walczak Geospatial Contrast Mining with Applications on Labeled Spatial Data, Wei Ding, Tomasz F. Stepinski, and Josue SalazarMining Emerging Patterns for Activity Recognition, Tao Gu, Zhanqing Wu, XianPing Tao, Hung Keng Pung, and Jian LuEmerging Pattern-Based Prediction of Heart Diseases and Powerline Safety, Keun Ho Ryu, Dong Gyu Lee, and Minghao PiaoEmerging Pattern-Based Crime Spots Analysis and Rental Price Prediction, Naoki Katoh and Atsushi Takizawa Survey of Other PapersOverview of Results on Contrast Mining and Applications, Guozhu Dong Bibliography Index

Guozhu Dong is a professor at Wright State University. A senior member of the IEEE and ACM, Dr. Dong holds four U.S. patents and has authored over 130 articles on databases, data mining, and bioinformatics; co-authored Sequence Data Mining; and co-edited Contrast Data Mining and Applications. His research focuses on contrast/emerging pattern mining and applications as well as first-order incremental view maintenance. He has a PhD in computer science from the University of Southern California. James Bailey is an Australian Research Council Future Fellow in the Department of Computing and Information Systems at the University of Melbourne. Dr. Bailey has authored over 100 articles and is an associate editor of IEEE Transactions on Knowledge and Data Engineering and Knowledge and Information Systems: An International Journal. His research focuses on fundamental topics in data mining and machine learning, such as contrast pattern mining and data clustering, as well as application aspects in areas, including health informatics and bioinformatics. He has a PhD in computer science from the University of Melbourne.