Exploiting the Power of Group Differences
Morgan & Claypool Publishers (Verlag)
978-1-68173-502-3 (ISBN)
This book presents pattern-based problem-solving methods for a variety of machine learning and data analysis problems. The methods are all based on techniques that exploit the power of group differences. They make use of group differences represented using emerging patterns (aka contrast patterns), which are patterns that match significantly different numbers of instances in different data groups. A large number of applications outside of the computing discipline are also included.
Emerging patterns (EPs) are useful in many ways. EPs can be used as features, as simple classifiers, as subpopulation signatures/characterizations, and as triggering conditions for alerts. EPs can be used in gene ranking for complex diseases since they capture multi-factor interactions. The length of EPs can be used to detect anomalies, outliers, and novelties. Emerging/contrast pattern based methods for clustering analysis and outlier detection do not need distance metrics, avoiding pitfalls of the latter in exploratory analysis of high dimensional data. EP-based classifiers can achieve good accuracy even when the training datasets are tiny, making them useful for exploratory compound selection in drug design. EPs can serve as opportunities in opportunity-focused boosting and are useful for constructing powerful conditional ensembles. EP-based methods often produce interpretable models and results. In general, EPs are useful for classification, clustering, outlier detection, gene ranking for complex diseases, prediction model analysis and improvement, and so on.
EPs are useful for many tasks because they represent group differences, which have extraordinary power. Moreover, EPs represent multi-factor interactions, whose effective handling is of vital importance and is a major challenge in many disciplines.
Based on the results presented in this book, one can clearly say that patterns are useful, especially when they are linked to issues of interest.
We believe that many effective ways to exploit group differences' power still remain to be discovered. Hopefully this book will inspire readers to discover such new ways, besides showing them existing ways, to solve various challenging problems.
Dr. Guozhu Dong is a professor of Computer Science and Engineering, and a member at the Knoesis Center of Excellence, at Wright State University. He received a Ph.D. in Computer Science from the University of Southern California and a B.S. in Mathematics from Shandong University. Before joining Wright State University, he was a faculty member at the University of Melbourne. His research interests span data mining, machine learning, databases, data science, bioinformatics, and artificial intelligence. He co-authored the book Sequence Data Mining; coedited two books, Contrast Data Mining and Feature Engineering, respectively; and authored the book Exploiting the Power of Group Differences. He is known for his pioneering work and sustained effort on emerging/contrast pattern mining and on the use of such patterns in problem solving. He has published hundreds of papers at major international conferences and in top-rate journals in the fields of data mining and databases. He received several best research paper awards at major data mining conferences. At Wright State University, he was recognized for Excellence in Research in his college. He has served on hundreds of program committees of international conferences, and he has chaired the program committees for several such conferences. He is a senior member of both ACM and IEEE. Jiawei Han is the Abel Bliss Professor in the Department of Computer Science, University of Illinois at Urbana-Champaign. He has been researching into data mining, information network analysis, database systems, and data warehousing, with over 900 journal and conference publications. He has chaired or served on many program committees of international conferences in most data mining and database conferences. He also served as the founding Editor-In-Chief of ACM Transactions on Knowledge Discovery from Data and the Director of Information Network Academic Research Center supported by U.S. Army Research Lab (2009-2016), and is the co-Director of KnowEnG, an NIH funded Center of Excellence in Big Data Computing since 2014. He is a Fellow of ACM, a Fellow of IEEE, and received 2004 ACM SIGKDD Innovations Award, 2005 IEEE Computer Society Technical Achievement Award, and 2009 M. Wallace McDowell Award from IEEE Computer Society. His co-authored book Data Mining: Concepts and Techniques has been adopted as a popular textbook worldwide. University of California, Santa Cruz Wei Wang is an associate professor in the Department of Computer Science and a member of the Carolina Center for Genomic Sciences at the University of North Carolina at Chapel Hill. She received a MS degree from the State University of New York at Binghamton in 1995 and a PhD degree in Computer Science from the University of California at Los Angeles in 1999. She was a research staff member at the IBM T. J. Watson Research Center between 1999 and 2002. Dr. Wang's research interests include data mining, bioinformatics, and databases. She has filed seven patents, and has published one monograph and more than one hundred research papers in international journals and major peer-reviewed conference proceedings. Dr. Wang received the IBM Invention Achievement Awards in 2000 and 2001. She was the recipient of a UNC Junior Faculty Development Award in 2003 and an NSF Faculty Early Career Development (CAREER) Award in 2005. She was named a Microsoft Research New Faculty Fellow in 2005. She was recently honored with the 2007 Phillip and Ruth Hettleman Prize for Artistic and Scholarly Achievement at UNC. Dr. Wang is an associate editor of the IEEE Transactions on Knowledge and Data Engineering and ACM Transactions on Knowledge Discovery in Data, and an editorial board member of the International Journal of Data Mining and Bioinformatics. She serves on the program committees of prestigious international conferences such as ACM SIGMOD, ACM SIGKDD, VLDB, ICDE, EDBT, ACM CIKM, IEEE ICDM, and SSDBM. Cornell University University of Chicago
Acknowledgments
Introduction and Overview
General Preliminaries
Emerging Patterns and a Flexible Mining Algorithm
CAEP: Classification By Aggregating Multiple Matching Emerging Patterns
CAEP for Classification on Tiny Training Datasets, Compound Selection, and Instance Selection
OCLEP: One-Class Intrusion Detection and Anomaly Detection
CPCQ: Contrast Pattern Based Clustering-Quality Evaluation
CPC: Pattern-Based Clustering
IBIG: Ranking Genes and Attributes for Complex Diseases and Complex Problems CPXR and CPXC: Pattern Aided Prediction Modeling and Prediction Model Analysis
Other Approaches and Applications Using Emerging Patterns
Bibliography
Author's Biography
Index
| Erscheinungsdatum | 06.03.2019 |
|---|---|
| Reihe/Serie | Synthesis Lectures on Data Mining and Knowledge Discovery |
| Mitarbeit |
Herausgeber (Serie): Jiawei Han, Lise Getoor, Wei Wang, Johannes Gehrke |
| Verlagsort | San Rafael |
| Sprache | englisch |
| Maße | 191 x 235 mm |
| Gewicht | 333 g |
| Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| ISBN-10 | 1-68173-502-4 / 1681735024 |
| ISBN-13 | 978-1-68173-502-3 / 9781681735023 |
| Zustand | Neuware |
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