Optimization Based Data Mining: Theory and Applications (Advanced Information and Knowledge Processing) - Softcover

Shi, Yong; Tian, Yingjie; Kou, Gang; Peng, Yi; Li, Jianping

Softcover

ISBN 10: 144712653X ISBN 13: 9781447126539

Verlag: Springer, 2013

Inhaltsangabe

Optimization techniques have been widely adopted to implement various data mining algorithms. In addition to well-known Support Vector Machines (SVMs) (which are based on quadratic programming), different versions of Multiple Criteria Programming (MCP) have been extensively used in data separations. Since optimization based data mining methods differ from statistics, decision tree induction, and neural networks, their theoretical inspiration has attracted many researchers who are interested in algorithm development of data mining.

Optimization based Data Mining: Theory and Applications, mainly focuses on MCP and SVM especially their recent theoretical progress and real-life applications in various fields. These include finance, web services, bio-informatics and petroleum engineering, which has triggered the interest of practitioners who look for new methods to improve the results of data mining for knowledge discovery.

Most of the material in this book is directly from the research and application activities that the authors’ research group has conducted over the last ten years. Aimed at practitioners and graduates who have a fundamental knowledge in data mining, it demonstrates the basic concepts and foundations on how to use optimization techniques to deal with data mining problems.

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Über die Autorin bzw. den Autor

Dr. Gang Kou is a professor of School of Management and Economics, University of Electronic Science and Technology of China and managing editor of International Journal of Information Technology & Decision Making. Previously, he was a research scientist in Thomson Co., R&D. He received his Ph.D. in Information Technology from the College of Information Science & Technology, Univ. of Nebraska at Omaha; got his Master degree in Dept of Computer Science, Univ. of Nebraska at Omaha; and B.S. degree in Department of Physics, Tsinghua University, Beijing, China. He has published more than eighty papers in various peer-reviewed journals and conferences. Gang Kou has been Keynote speaker/workshop chair in several international conferences. He co-chaired Data Mining contest on The Seventh IEEE International Conference on Data Mining 2007 and he is the Program Committee Co-Chair of the 20th International Conference on Multiple Criteria Decision Making (2009) and NCM 2009: 5th International Joint Conference on INC, ICM and IDC.  He is also co-editor of special issues of several journals, such as Journal of Multi Criteria Decision Analysis, Decision Support Systems, Journal of Supercomputing and Information Sciences. He accomplished more than 300 cites of published journal articles as shown in the Science Citation Index (SCI) database.   Daji Ergu, a PhD candidate in Management Science and Engineering, College of Economics & Management, University of electronic science and technology of China. He has been a lecturer of Engineering Mathematics, College of Electrical Information & Technology, Southwest University for Nationalities since 2003. Ergu's research interests include multiple criteria decision making, risk analysis and data mining. He has published 9 papers, and 4 of which collected in SCI/EI Indexes.   Dr. Yi Peng is a Professor of School of Management and Economics, Universityof Electronic Science and Technology of China. Previously, she worked as Senior Analyst for West Co., USA. Dr. Peng received her Ph.D. in Information Technology from the College of Information Science & Technology, Univ. of Nebraska at Omaha and got her Master degree in Dept of Info. Science & Quality Assurance, Univ. of Nebraska at Omaha and B.S. degree in Department of Management Information Systems, Sichuan University, China. Dr. Peng's research interests cover Knowledge Discover in Database and data mining, multi-criteria decision making, data mining methods and modeling, knowledge discovery in real-life applications. She published more than sity papers in various peer-reviewed journals and conferences. She is the Workshop Chair of the 20th International Conference on Multiple Criteria Decision Making (2009), guest editor of Annals of Operations Research's special issue on Multiple Criteria Decision Making on Operations Research.   Dr. Yong Shi, Senior Member of IEEE, serves as the Executive Deputy Director, Chinese Academy of Sciences Research Center on Fictitious Economy & Data Science. He has been the Charles W. and Margre H. Durham Distinguished Professor of Information Technology, College of Information Science and Technology, Peter Kiewit Institute, University of Nebraska, USA since 1999. Dr. Shi's research interests include business intelligence, data mining, and multiple criteria decision making. He has published more than 17 books, over 200 papers in various journals and numerous conferences/proceedings papers. He is the Editor-in-Chief of International Journal of Information Technology and Decision Making (SCI), and a member of Editorial Board for a number of academic journals. Dr. Shi has received many distinguished awards including the Georg Cantor Award of the International Society on Multiple Criteria Decision Making (MCDM), 2009; Fudan Prize of Distinguished Contribution in Management, Fudan Premium Fund ofManagement, China, 2009; Outstanding Young Scientist Award, National Natural Science Foundation of Chin

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Optimization techniques have been widely adopted to implement various data mining algorithms. In addition to well-known Support Vector Machines (SVMs) (which are based on quadratic programming), different versions of Multiple Criteria Programming (MCP) have been extensively used in data separations. Since optimization based data mining methods differ from statistics, decision tree induction, and neural networks, their theoretical inspiration has attracted many researchers who are interested in algorithm development of data mining.

Optimization based Data Mining: Theory and Applications, mainly focuses on MCP and SVM especially their recent theoretical progress and real-life applications in various fields. These include finance, web services, bio-informatics and petroleum engineering, which has triggered the interest of practitioners who look for new methods to improve the results of data mining for knowledge discovery.

Most of the material in this book is directly from the research and application activities that the authors’ research group has conducted over the last ten years. Aimed at practitioners and graduates who have a fundamental knowledge in data mining, it demonstrates the basic concepts and foundations on how to use optimization techniques to deal with data mining problems.

Details zur Ausgabe

• Verlag: Springer
• Erscheinungsdatum: 2013
• Sprache: Englisch
• ISBN 10: 144712653X
• ISBN 13: 9781447126539
• Einband: Taschenbuch
• Anzahl der Seiten: 332
• Kontakt zum Hersteller: ProductSafety@springernature.com; ProductSafety@springernature.com; ; Poststr. 9; Darmstadt; 64293; Deutschland