期刊文献+

《数学规划》课程教学中可引入的若干数据挖掘问题

Several Involved Data Mining Problems in the Teaching of Mathematical Programming Curriculum
下载PDF
导出
摘要 数据挖掘技术用于在海量数据中发现有用的知识,它涉及许多领域,其中数学规划方法成为数据挖掘领域的重要工具.对《数学规划》课程教学中可以引入的分类、特征选择、聚类和回归等数据挖掘基本问题进行了讨论. Data mining is used for discovering new knowledge from massive data involving multiple disciplines, and mathematical programming methods is becoming an important tool for data mining. Several essential problems such as classification, feature selection, clustering and regression in data mining are discussed, which can be introduced into the teachhag of mathematical programming curriculum programming.
作者 宋杰
机构地区 韶关学院数学系
出处 《韶关学院学报》 2005年第9期10-14,共5页 Journal of Shaoguan University
关键词 数学规划 数据挖掘 分类 特征选择 聚类 回归 教学 mathematical programming data mining classification feature selection clustering regression teaching
  • 相关文献

参考文献4

  • 1P.S. Bradley,O.L. Mangasarian,J.B. Rosen. Parsimonious Least Norm Approximation[J] 1998,Computational Optimization and Applications(1):5~21
  • 2W. N. Street,O. L. Mangasarian. Improved Generalization via Tolerant Training[J] 1998,Journal of Optimization Theory and Applications(2):259~279
  • 3O.L. Mangasarian. Mathematical Programming in Data Mining[J] 1997,Data Mining and Knowledge Discovery(2):183~201
  • 4Kristin P. Bennett,O. L. Mangasarian. Bilinear separation of two sets inn-space[J] 1993,Computational Optimization and Applications(3):207~227

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部