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Introduction to Data Mining Techniques via Multiple Criteria Optimization Approaches and Applications
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Author(s): Yong Shi (University of the Chinese Academy of Sciences, China, and University of Nebraska at Omaha, USA), Yi Peng (University of Nebraska at Omaha, USA), Gang Kou (University of Nebraska at Omaha, USA)and Zhengxin Chen (University of Nebraska at Omaha, USA)
Copyright: 2009
Pages: 25
Source title:
Data Mining Applications for Empowering Knowledge Societies
Source Author(s)/Editor(s): Hakikur Rahman (Ansted University Sustainability Research Institute, Malaysia)
DOI: 10.4018/978-1-59904-657-0.ch001
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Abstract
This chapter provides an overview of a series of multiple criteria optimization-based data mining methods, which utilize multiple criteria programming (MCP) to solve data mining problems, and outlines some research challenges and opportunities for the data mining community. To achieve these goals, this chapter first introduces the basic notions and mathematical formulations for multiple criteria optimization- based classification models, including the multiple criteria linear programming model, multiple criteria quadratic programming model, and multiple criteria fuzzy linear programming model. Then it presents the real-life applications of these models in credit card scoring management, HIV-1 associated dementia (HAD) neuronal damage and dropout, and network intrusion detection. Finally, the chapter discusses research challenges and opportunities.
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