Contrast Data Mining
Concepts, Algorithms, and Applications
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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.
Table of Contents
Preliminaries and Statistical Contrast Measures
Preliminaries, Guozhu Dong
Statistical Measures for Contrast Patterns, James Bailey
Contrast Mining Algorithms
Mining Emerging Patterns Using Zero-Suppressed Binary Decision Diagrams, James Bailey and Elsa Loekito
Efficient Direct Mining of Selective Discriminative Patterns for Classification, Hong Cheng, Jiawei Han, Xifeng Yan, and Philip S. Yu
Mining Emerging Patterns from Structured Data, James Bailey
Incremental Maintenance of Emerging Patterns, Mengling Feng and Guozhu Dong
Generalized Contrasts, Emerging Data Cubes, and Rough Sets
Emerging Data Cube Representations for OLAP Database Mining, Sébastien Nedjar, Lotfi Lakhal, and Rosine Cicchetti
Relation between Jumping Emerging Patterns and Rough Set Theory, Pawel Terlecki and Krzysztof Walczak
Contrast Mining for Classification and Clustering
Overview and Analysis of Contrast Pattern-Based Classification, Xiuzhen Zhang and Guozhu Dong
Using Emerging Patterns in Outlier and Rare-Class Prediction, Lijun Chen and Guozhu Dong
Enhancing Traditional Classifiers Using Emerging Patterns, Guozhu Dong and Kotagiri Ramamohanarao
CPC: A Contrast Pattern-Based Clustering Algorithm, Neil Fore and Guozhu Dong
Contrast Mining for Bioinformatics and Chemoinformatics
Discriminating Gene Transfer and Microarray Concordance Analysis, Shihong Mao and Guozhu Dong
Toward Mining Optimal Emerging Patterns amid 1000s of Genes, Shihong Mao and Guozhu Dong
Emerging Chemical Patterns — Theory and Applications, Jens Auer, Martin Vogt, and Jürgen Bajorath
Emerging Patterns as Structural Alerts for Computational Toxicology, Bertrand Cuissart, Guillaume Poezevara, Bruno Crémilleux, Alban Lepailleur, and Ronan Bureau
Contrast Mining for Special Domains
Mining Emerging Patterns for Activity Recognition, Tao Gu, Zhanqing Wu, XianPing Tao, Hung Keng Pung, and Jian Lu
Emerging Pattern-Based Prediction of Heart Diseases and Powerline Safety, Keun Ho Ryu, Dong Gyu Lee, and Minghao Piao
Emerging Pattern-Based Crime Spots Analysis and Rental Price Prediction, Naoki Katoh and Atsushi Takizawa
Survey of Other Papers
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.
This book, edited by two leading researchers on contrast mining, Professors Guozhu Dong and James Bailey, and contributed to by over 40 data mining researchers and application scientists, is a comprehensive and authoritative treatment of this research theme. It presents a systematic introduction and a thorough overview of the state of the art for contrast data mining, including concepts, methodologies, algorithms, and applications. … the book will appeal to a wide range of readers, including data mining researchers and developers who want to be informed about recent progress in this exciting and fruitful area of research, scientific researchers who seek to find new tools to solve challenging problems in their own research domains, and graduate students who want to be inspired on problem solving techniques and who want to get help with identifying and solving novel data mining research problems in various domains.
—From the Foreword by Jiawei Han, University of Illinois, Urbana-Champaign, USA