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首页> 外文期刊>International Journal of Pattern Recognition and Artificial Intelligence >Clustering Quality Measures Based On Comparing The Proximity Matrices For The Membership Vectors And The Objects
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Clustering Quality Measures Based On Comparing The Proximity Matrices For The Membership Vectors And The Objects

机译：基于成员向量和对象的邻近矩阵比较的聚类质量测度

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摘要

There are several commonly accepted clustering quality measures (clustering quality as opposed to cluster quality) such as the rand index, the adjusted rand index and the jacquard index. Each of these however is based on comparing the partition produced by the clustering process to a correct partition. They can therefore only be used to determine the quality of a clustering process when the correct partition is known. This paper therefore proposes another clustering quality measure that does not require the comparison to a correct partition. The proposed metric is based on the assumption that the proximities between the membership vectors should correlate positively with the proximities between the objects which may be the proximities between their feature vectors. The values of the components of the membership vector, corresponding to a pattern, are the membership degrees of the pattern in the various clusters. The membership vector is just another object data vector or type of feature vector with the feature values for an object being the membership values of the object in the various clusters. Based on this premise, this paper describes some new cluster quality metrics derived from standard correlation measures and other proposed correlation metrics. Simulations on data with a wide range of clusterability or separability show that the approach of comparing the proximity matrix based on the membership matrix to the object proximity matrix is quite effective.
机译：有几种公认的聚类质量度量（聚类质量而不是聚类质量），例如rand指数，调整后的rand指数和提花指数。但是，每个方法都基于将聚类过程产生的分区与正确的分区进行比较。因此，只有在知道正确的分区时，它们才能用于确定群集过程的质量。因此，本文提出了另一种聚类质量度量，不需要与正确分区进行比较。提出的度量基于以下假设：隶属向量之间的邻近度应与对象之间的邻近度正相关，而对象之间的邻近度可能是其特征向量之间的邻近度。隶属向量的分量的对应于模式的值是该模式在各个聚类中的隶属度。隶属度矢量仅仅是另一种对象数据矢量或特征矢量的类型，其中对象的特征值是各个聚类中对象的隶属度值。在此前提下，本文介绍了一些从标准相关性度量和其他建议的相关性度量中得出的新的群集质量度量。对具有大范围可聚性或可分离性的数据进行的仿真表明，将基于隶属度矩阵的接近度矩阵与对象接近度矩阵进行比较的方法非常有效。

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