关键词: 聚类算法, 初始中心点, 密度, 混合属性

Abstract:

There are some problems in the K-Prototypes clustering algorithm, such as manually specifying the initial clustering center and the number of clusters, which will lead to low accuracy and stability of the algorithm. In order to solve these problems, this paper proposes a K-Prototypes clustering algorithm based on density optimization, which can adaptively optimize the setting of the number of clusters and the initial clustering according to the distribution density of data objects, and can improve the accuracy of clustering by distinguishing the different influence weights of each attribute on clustering results and improve the distance calculation formula by distinguishing the different influence weights of each attribute on clustering results, which will improve the accuracy of clustering. The experimental results on synthetic data set and UCI data set show that the proposed method achieves better clustering results. Compared with K-Prototypes, DPCM and Fuzzy K-Prototypes, the average accuracy of the proposed method is improved by 8.52%, 4.28% and 8.33% respectively.

Key words: clustering algorithm, initial center points, density peak, mixed attributes