Optimization Based Data Mining: Theory and Applications
Optimization Based Data Mining: Theory and Applications
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DOI：
10.1007/978-0-85729-504-0
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发表时间：
2011-05
期刊：
影响因子：
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通讯作者：
Yong Shi;Ying-jie Tian;Gang Kou;Yi Peng;Jianping Li
中科院分区：
文献类型：
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作者：
Yong Shi;Ying-jie Tian;Gang Kou;Yi Peng;Jianping Li
优化技术已被广泛采用，以实现各种数据挖掘算法。除了众所周知的支持向量机（SVM）（基于二次规划），不同版本的多准则规划（MCP）已被广泛用于数据分离。由于基于优化的数据挖掘方法不同于统计学、决策树归纳法和神经网络等方法，其理论启发吸引了许多对数据挖掘算法开发感兴趣的研究者。基于优化的数据挖掘：理论与应用，主要介绍了MCP和SVM的理论进展和在各个领域的实际应用。其中包括金融，网络服务，生物信息学和石油工程，这引发了从业者的兴趣，他们寻找新的方法来改善数据挖掘的结果，以发现知识。本书中的大部分材料直接来自作者的研究小组在过去十年中进行的研究和应用活动。针对从业者和毕业生谁拥有数据挖掘的基础知识，它演示了如何使用优化技术来处理数据挖掘问题的基本概念和基础。
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.