一种增强的K-prototypes混合数据聚类算法  

An enhanced K-prototypes mixed data clustering algorithm

作　　者：李顺勇[1] 顾嘉成 LI Shun-yong;GU Jia-cheng(School of Mathematical Sciences, Shanxi University, Taiyuan 030006, China)

机构地区：[1]山西大学数学科学学院,山西太原030006

出　　处：《陕西科技大学学报》2021年第2期183-188,共6页Journal of Shaanxi University of Science & Technology

基　　金：山西省留学人员科技活动择优资助项目(2019-13);山西省基础研究计划项目(201901D111320);山西省太原市科技计划项目(2018140105000084);山西省高等学校精品共享课程(K2020022)。

摘　　要：针对K-prototypes算法处理混合数据时精度不高等问题,提出了一种增强的K-prototypes混合数据聚类算法(An Enhanced K-prototypes Mixed Data Clustering Algorithm,EKPCA).首先定义了一种新的距离计算公式,扩大了数据之间的差异性,有利于对簇边缘数据进行合理划分;其次选取较多初始原型来覆盖数据的整体信息;最后迭代消去多余原型,得到数据集的真实分类.在8个UCI数据集上对算法进行评测,实验结果表明EKPCA算法有较高聚类精度.Aiming at the problem that K-prototypes algorithm is not accurate in processing mixed data,an enhanced K-prototypes mixed data clustering algorithm(EKPCA)is proposed.Firstly,a new distance calculation formula is defined to expand the difference between the data,which is conducive to the reasonable division of cluster edge data.Secondly,more initial prototypes are selected to cover the overall information of the data.Finally,the redundant prototypes are eliminated iteratively to obtain the real classification of the data set.The algorithm is evaluated on 8 UCI datasets,and the experimental results show that EKPCA algorithm has high clustering accuracy.

关 键 词：K-prototypes 混合数据 距离计算 初始原型 迭代消去 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]