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| 基于距离的粗糙集属性约减改进算法研究 | |
| 引用本文： | 徐贤清,马良荔,时扬. 基于距离的粗糙集属性约减改进算法研究[J]. 计算机与数字工程, 2010, 38(11): 48-51 |
| 作者姓名： | 徐贤清 马良荔 时扬 |
| 作者单位： | [1]海军工程大学计箅机系,武汉430033 [2]海军工程大学训练部,武汉430033 |
| 摘 要： | 属性约减是粗糙集理论的重要研究内容之一。由于Z.Pawlak经典粗糙集模型在处理集合间隶属关系过于简单的缺陷,文章提出了以集合间距离作为集合隶属关系的判别依据,对属性依赖度和重要度重新进行了定义,从而对属性约减算法进行改进。最后,通过一个数据模型的验证,改进后的算法能够更有效地滤除冗余属性,保留关键属性。 |
| 关 键 词： | 粗糙集 数据挖掘 属性约减 聚类分析 距离 |
Research on Improve Attribute Reduction Algorithm of Rough Set Based on Distance |
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| Xu Xianqing,Ma Liangli,Shi Yang. Research on Improve Attribute Reduction Algorithm of Rough Set Based on Distance[J]. Computer and Digital Engineering, 2010, 38(11): 48-51 | |
| Authors: | Xu Xianqing Ma Liangli Shi Yang |
| Affiliation: | Xu Xianqing) Ma Liangli) Shi Yang)(Computer Department,Naval University of Engineering1),Wuhan 430033)(Command Department,Naval University of Engineering2),Wuhan 430033) |
| Abstract: | Knowledge reduction is one of the most important issues in rough sets theory.Because of the defects that Z.Pawlak rough set model in dealing with the collection of affiliation is too simple,this paper put forward to use collection＇s distance to discriminate collection,then redefine the dependence and importance of attribute,so improve the attribute reduction algorithm.Finally,under the validation of a data model,the improved algorithm can effectively filter out redundant properties,retain key attributes. |
| Keywords: | yough set data mining attribute reduction cluster analysis distance |
| 本文献已被 维普 万方数据 等数据库收录！ | |