基于粗糙集的属性约简在数据挖掘中的应用研究

Study on Application of Attributive Reduction Based on Rough Sets in Data Mining

属性约简是粗糙集理论知识获取中的关键问题之一。先利用差别矩阵求得核属性,再利用属性的重要度作为启发式去求约简,可取得合理的属性组合,避免了基于代数方法与基于信息熵方法的复杂运算。最后通过实例分析验证了算法的有效性与可行性。

The attribute reduction is one of the key issues on knowledge acquisition in rough set theory. In this paper, the discernibility matrix was utilized to obtain the core attribute first, then a new heuristic algorithm for attribute reduc- tion was proposed based on the importance ratings of the attribute. Our algorithm can give proper combination of attri- butes for effective attribute reduction and therefore the complex computing is avoided, which is different from some methods relying on algebra and information entropy. Finally, the example analysis demonstrates the validity and feasibi- lity of our algorithnx