论文标题
K-均值算法输出的聚类保留转换
A Clustering Preserving Transformation for k-Means Algorithm Output
论文作者
论文摘要
本说明介绍了一种新颖的聚类保存从$ k $ -MEANS算法获得的集群集的转换。此转换可用于生成来自现有数据的新标记数据{}集合。基于Kleinberg Axiom的一致性转换更为灵活，因为可以移开群集中的数据点，并且群集之间的数据点可能会更加近。
This note introduces a novel clustering preserving transformation of cluster sets obtained from $k$-means algorithm. This transformation may be used to generate new labeled data{}sets from existent ones. It is more flexible that Kleinberg axiom based consistency transformation because data points in a cluster can be moved away and datapoints between clusters may come closer together.
