Cluster Validation by Measurement of Clustering Characteristics Relevant to the User
Cluster Validation by Measurement of Clustering Characteristics Relevant to the User
复制标题
通过测量与用户相关的聚类特征来进行聚类验证
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
C. Hennig
中科院分区：
文献类型：
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作者：
C. Hennig
有许多聚类分析方法可以在同一数据集上产生完全不同的聚类。聚类验证是关于聚类质量的评估;“相对聚类验证”是关于使用这样的标准来比较聚类。这可以用于从不同方法的一组聚类中选择一个，或者从使用不同参数（例如不同数量的聚类）运行的同一方法中选择一个。
文献中有许多聚类验证指标。他们中的大多数试图通过单个数字来衡量聚类的整体质量，但这可能是不合适的。根据聚类的目的，聚类有各种不同的特性，例如低的聚类内距离和高的聚类间分离。
在本文中，一些验证标准将被引入，指的是不同的期望特性的聚类，并在多维的方式来解释聚类。在特定应用中，用户可能对这些标准中的一些而不是其他标准感兴趣。本文的一个重点是对方法来概括不同的特点，使用户可以聚合它们以适当的方式指定权重的各种标准，是相关的聚类应用程序在手。
There are many cluster analysis methods that can produce quite different clusterings on the same dataset. Cluster validation is about the evaluation of the quality of a clustering; "relative cluster validation" is about using such criteria to compare clusterings. This can be used to select one of a set of clusterings from different methods, or from the same method ran with different parameters such as different numbers of clusters.
There are many cluster validation indexes in the literature. Most of them attempt to measure the overall quality of a clustering by a single number, but this can be inappropriate. There are various different characteristics of a clustering that can be relevant in practice, depending on the aim of clustering, such as low within-cluster distances and high between-cluster separation.
In this paper, a number of validation criteria will be introduced that refer to different desirable characteristics of a clustering, and that characterise a clustering in a multidimensional way. In specific applications the user may be interested in some of these criteria rather than others. A focus of the paper is on methodology to standardise the different characteristics so that users can aggregate them in a suitable way specifying weights for the various criteria that are relevant in the clustering application at hand.