一种信息论的外部竞争者有效性测度
An Information-Theoretic External Cluster-Validity Measure
摘要：
在本文中，我们提出了一个衡量聚类质量或准确性，这是适当的情况下，它是可取的，以评估聚类算法，以某种方式比较它产生的集群与“地面真相”组成的类分配给模式，通过手动手段或其他一些手段，其准确性有信心。这些措施被称为"外部“措施。我们的措施也有特点，允许聚类与不同数量的集群进行比较，在定量和原则的方式。我们的评估计划定量测量如何有用的模式的集群标签作为其类标签的预测。在要比较的所有聚类具有相同数目的聚类的情况下，该度量等同于聚类标签和类标签之间的互信息。然而，在簇的数量不同的情况下，如果编码器和解码器都可以自由访问簇标签，则它计算编码（压缩）类标签所需的比特数量的减少。为了实现这种编码，还必须对给定聚类标签的类标签的估计条件概率进行编码。这些估计的概率可以被看作是类标签的模型，并且它们的相关代码长度作为模型成本。
英文摘要：
In this paper we propose a measure of clustering quality or accuracy that is appropriate in situations where it is desirable to evaluate a clustering algorithm by somehow comparing the clusters it produces with ``ground truth' consisting of classes assigned to the patterns by manual means or some other means in whose veracity there is confidence. Such measures are refered to as ``external'. Our measure also has the characteristic of allowing clusterings with different numbers of clusters to be compared in a quantitative and principled way. Our evaluation scheme quantitatively measures how useful the cluster labels of the patterns are as predictors of their class labels. In cases where all clusterings to be compared have the same number of clusters, the measure is equivalent to the mutual information between the cluster labels and the class labels. In cases where the numbers of clusters are different, however, it computes the reduction in the number of bits that would be required to encode (compress) the class labels if both the encoder and decoder have free acccess to the cluster labels. To achieve this encoding the estimated conditional probabilities of the class labels given the cluster labels must also be encoded. These estimated probabilities can be seen as a model for the class labels and their associated code length as a model cost.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
