Improved K-modes clustering algorithm based on rough entropy
At present,K-modes clustering algorithm is widely used in artificial intelligence,data mining and other fields.The traditional K-modes clustering algorithm has good clustering effect,but it also faces too many iterations,large amount of calculation,easy to be interfered by redundant attributes and other problems.In addition,only a simple 0-1 matching method is used to define the distance between the attribute values of each two samples,without fully considering the influence of each attribute on the clustering results.To solve the above problems,this paper introduces the rough entropy into K-modes algorithm.Firstly,the attribute reduction algorithm of rough set is used to eliminate redundant attributes and determine the importance of each attribute.Then,the rough entropy is used to determine the weight of each attribute,so as to define a new intra-class distance.In this paper,the proposed algorithm was compared with the traditional K-modes algorithm on four groups of public data sets respectively.The experimental results show that the proposed algorithm has higher clustering accuracy than the traditional K-modes algorithm.
万方数据
维普
