Optimization Based Data Mining: Theory and Applications (Advanced Information and Knowledge Processing) by Yong Shi, Yingjie Tian, Gang Kou, Yi Peng, Jianping Li

Optimization Based Data Mining: Theory and Applications (Advanced Information and Knowledge Processing) by Yong Shi, Yingjie Tian, Gang Kou, Yi Peng, Jianping Li
Item# 13020640177
Retail price: US$99.00
Sale price: US$8.00

all items in this store are to be sent to your email within 24 hours after cleared payment. PDF eBooks are sent to you as email attachments. as for mp3 audiobook, a download link from ONEDRIVE will be sent to your email for you to download.

Please Read Before Your Purchase!!!

1. This item is an E-Book in PDF format.

2. Shipping & Delivery: Send to you by E-mail within 24 Hours after cleared payment. Immediately Arrival!!!

3. Shipping ( by email) + Handling Fee = US$0.00

4. Time-Limited Offer, Order Fast.

*************************************************************************









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

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



Publisher: Springer; 2011 edition (May 20, 2011)



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.



From the Back Cover 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.