β Scribed by Yong Shi, Yingjie Tian, Gang Kou, Yi Peng, Jianping Li (auth.)
No coin nor oath required. For personal study only.
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 authorsβ 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.
Front Matter....Pages I-XV
Front Matter....Pages 1-1
Support Vector Machines for Classification Problems....Pages 3-13
LOO Bounds for Support Vector Machines....Pages 15-46
Support Vector Machines for Multi-class Classification Problems....Pages 47-60
Unsupervised and Semi-supervised Support Vector Machines....Pages 61-79
Robust Support Vector Machines....Pages 81-105
Feature Selection via l p -Norm Support Vector Machines....Pages 107-116
Front Matter....Pages 117-117
Multiple Criteria Linear Programming....Pages 119-132
MCLP Extensions....Pages 133-156
Multiple Criteria Quadratic Programming....Pages 157-170
Non-additive MCLP....Pages 171-181
MC2LP....Pages 183-192
Front Matter....Pages 193-193
Firm Financial Analysis....Pages 195-201
Personal Credit Management....Pages 203-231
Health Insurance Fraud Detection....Pages 233-235
Network Intrusion Detection....Pages 237-241
Internet Service Analysis....Pages 243-248
HIV-1 Informatics....Pages 249-258
Anti-gen and Anti-body Informatics....Pages 259-267
Geochemical Analyses....Pages 269-275
Intelligent Knowledge Management....Pages 277-293
Back Matter....Pages 295-316
Data Mining and Knowledge Discovery; Input/Output and Data Communications
π SIMILAR VOLUMES
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.
<p><P>The importance of having efficient and effective methods for data mining and knowledge discovery (DM) is rapidly growing. This is due to the wide use of fast and affordable computing power and data storage media and also the gathering of huge amounts of data in almost all aspects of human acti
The importance of having ef cient and effective methods for data mining and kn- ledge discovery (DM&KD), to which the present book is devoted, grows every day and numerous such methods have been developed in recent decades. There exists a great variety of different settings for the main problem stud
<p>Β·Β Β Β Β Β Β Β Β This book is an updated version of a well-received book previously published in Chinese by Science Press of China (the first edition in 2006 and the second in 2013). It offers a systematic and practical overview of spatial data mining, which combines computer science and geo-spatial info