Abstract: “Regular cluster regions” of data-point sets containing both order attributes and out-of-order attributes are obtained by applying decision tree method.Feature weight of both order attributes and out-of-order attributes can be optimized separately by using the principia of “data points in any cluster are close to each other，and data points between any two clusters are away from each other”.This optimization method of feature weight based on decision tree division can optimize feature weight of data sets with hybrid attributes.

摘要： 应用决策树方法来获取混合属性数据点集的“规则聚类区域”，利用“异类子聚类相离，同类子聚类相近”的原则来交替优化有序属性和无序属性的权重，提出了基于决策树划分的特征权重优化方法。该方法在一定程度上解决了有效获取数据子集的子聚类问题和混合属性数据点集的特征权重优化难题。仿真实验表明，该方法在优化混合属性数据点集的特征权重时是有效的。