Novel Autonomous Clustering Method Based on Decision-theoretic Rough Set
一种基于决策粗糙集的自动聚类方法

This paper proposed an autonomous knowledge-oriented clustering method based on decision-theoretic rough set model. In order to obtain the initial clustering, the initial threshold values need to set in the knowledgcoricnted clustering framework. Thus, a novel method, sort difference, was proposed to produce the initial threshold values autonomously in view of physics theory. Then, a cluster validity index based on the decision-theoretic rough set model was developed by considering various loss functions, which can estimate the quality of clustering.The results of experiments show that the new approach is valuable.