基于属性区分能力和AP聚类的属性粒化方法 (Attribute Granulation Based on Attribute Discernibility and AP Clustering)

Hong Zhu, Shifei Ding

Abstract:

This paper put forward a kind of attribute granulation method based on attribute discernibility and AP clustering.The method calculates the similarity of attributes according to attribute discernibility firstly,and then clusters attributes into several groups through affinity propagation clustering algorithm.At last,representative attributes are produced through some algorithms to form a coarser attribute granularity.The method is more efficient than traditional attribute reduction algorithm for large data set.It has obvious advantages under the condition of less strict precision of attribute granularity.

提出了一种基于属性区分能力和AP聚类的属性粒化方法(Attribute Granulation based on attribute discernibility and AP algorithm,AGAP)。该方法首先依据属性依赖度计算属性的区分能力；然后将所有属性作为潜在的聚类中心,使用AP算法聚类,得到若干个属性簇类；最后采取选用代表属性的方法得到较粗的属性粒子,从而达到属性粗粒化的要求。对高维数据的特征降维,这种算法比传统的属性约简算法大大提高了运算效率,在属性粒化精度要求不是很严格的情况下,所提算法优势明显。