摘要： 现有面向矩阵数据集的算法多数通过随机选取初始类中心得到聚类结果。为克服不同初始类中心对聚类结果的影响，针对分类型矩阵数据，提出一种新的初始聚类中心选择算法。根据属性值的频率定义矩阵对象的密度和矩阵对象间的距离，扩展最大最小距离算法，从而实现初始类中心的选择。在7个真实数据集上的实验结果表明，与初始类中心选择算法CAOICACD和BAIICACD相比，该算法均具有较优的聚类效果。

关键词: 分类型矩阵, 矩阵对象, 初始聚类中心, 密度, 数据集

Abstract: Most of the existing algorithms for matrix datasets obtain clustering results by randomly selecting the initial cluster center.In order to overcome the influence of different initial cluster centers on the clustering results,this paper proposes a new initial cluster center selection algorithm for categorical matrix data.The density of the matrix objects and the distance between the matrix objects are defined according to the frequency of the attribute values,and the maximum and minimum distance algorithms are extended to realize the selection of the initial cluster center.Experimental results on seven real datasets show that the algorithm has better clustering effect than the initial cluster center selection algorithms CAOICACD and BAIICACD.

Key words: categorical matrix, matrix object, initial cluster center, density, datasets

中图分类号: 

• TP391