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一种聚类分离度量。

A cluster separation measure.

机构信息

Department of Electrical Engineering, University of Tennessee, Knoxville, TN 37916; 17 C Downey Drive, Manchester, CT 06040.

出版信息

IEEE Trans Pattern Anal Mach Intell. 1979 Feb;1(2):224-7.

Abstract

A measure is presented which indicates the similarity of clusters which are assumed to have a data density which is a decreasing function of distance from a vector characteristic of the cluster. The measure can be used to infer the appropriateness of data partitions and can therefore be used to compare relative appropriateness of various divisions of the data. The measure does not depend on either the number of clusters analyzed nor the method of partitioning of the data and can be used to guide a cluster seeking algorithm.

摘要

提出了一种度量方法,用于衡量假设具有数据密度的聚类之间的相似性,该密度是从聚类特征向量到距离的递减函数。该度量方法可用于推断数据分区的适当性,因此可用于比较数据各种划分的相对适当性。该度量方法既不依赖于分析的聚类数量,也不依赖于数据的分区方法,可用于指导聚类搜索算法。

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