| 基于面向属性泛化及信息增益的数据挖掘方法研究 |
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| 引用本文： | 张文宇,张铭华. 基于面向属性泛化及信息增益的数据挖掘方法研究[J]. 计算机应用, 2006, 26(4): 861-863 |
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| 作者姓名： | 张文宇 张铭华 |
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| 作者单位： | 西安邮电学院,管理系,陕西,西安,710061;西安邮电学院,管理系,陕西,西安,710061 |
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| 摘 要： | 针对面向属性的归纳方法及粗糙集方法对知识粒性连续性的特点，将两者有机结合，利用面向属性归纳方法对数据进行泛化，再用属性的信息增益技术寻找泛化属性之间的数据依赖关系，能快速地在数据集中挖掘分类规则。将其应用于经典的仿真算例中，仿真结果合理、可靠。
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| 关 键 词： | 数据挖掘 粗糙集 属性泛化 信息增益 |
| 文章编号： | 1001-9081（2006）04-0861-03 |
| 收稿时间： | 2005-10-01 |
| 修稿时间： | 2005-10-012005-12-14 |
Research on data mining method based on attribute-oriented generalization and information gain |
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| Zhang Wen-yu,Zhang Ming-hua. Research on data mining method based on attribute-oriented generalization and information gain[J]. Journal of Computer Applications, 2006, 26(4): 861-863 |
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| Authors: | Zhang Wen-yu Zhang Ming-hua |
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| Abstract: | According to the characteristics of attribute-oriented generalization method and knowledge granule successive based on rough set, after organically combining these two method, the classification rules could be rapidly mined in database. The method of attribute-oriented generalization was firstly used to generalize the dada, and then the technology of information gain was utilized to find the dependency relation of data. The algorithm was applied in the classical simulation example and the result is rational and reliable. |
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| Keywords: | data mining rough set attribute generalization information gain |
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