数据聚类分析新方法研究

信息技术不断的进步，现实世界中需要处理的数据已由单一的数值型逐渐转变成由数值、文本、符号等类型构成的混合型数据。与现存大量的面向数值型数据的聚类算法相比，能有效处理混合型数据的聚类算法相对较少。为此，在格论基础上提出了一种适用于混合数据的聚类算法，该算法根据对象间格的覆盖数量来度量相似度，根据高覆盖数高相似度的原则选择聚类中心进行聚类。实验结果表明与其他传统聚类算法相比，新算法在不增加空间复杂度的情况下有效地提高了聚类的质量。

New clustering algorithm based on lattice

With the development of information technology,mixed data should be processed which changes from the simple numerical or categorical to mixed value.Compared with clustering methods for pure numerical data,the methods can process mixed numerical effectively are very few.This paper presents a new algorithm based on lattice for multiple-attribute which uses lattice covers to calculate the similarity between two objects.In the algorithm,each sample will be distributed to the cluster where it gets the maximum co...