中文摘要：

针对不确定分类数据,基于Squeezer算法提出一种有效的不确定数据聚类算法：USqueezer算法。该算法先计算一个不确定分类数据与每个簇的相似度概率和,选取最大的相似度和给定的阈值相比较,若大于阈值,将不确定数据划分到该簇中,否则创建一个新簇。实验表明,USqueezer算法能够有效地进行不确定分类数据的聚类,并且占用较少的运行内存空间和运行时间。

英文摘要：

Aimed at processing the uncertain categorical data,an efficient uncertain data clustering algorithm,the USqueezer algorithm,was proposed based on the squeezer algorithm.First,this algorithm computed the sum of similarity probability between uncertain categorical data and each existing cluster.Comparing the largest similarity with a given threshold,it was found that if the largest similarity was greater than the threshold value,the uncertain data would be assigned to this cluster,otherwise the uncertain categorical data was created as a new cluster.Experimental results showed that this algorithm could be effectively used in clustering the uncertain categorical data with a small amount of memory and time.