Cluster Purging：基于率失真理论的有效离群点检测
Cluster Purging: Efficient Outlier Detection based on Rate-Distortion Theory
摘要：
基于率失真理论的离群值检测建立在良好的数据压缩将用唯一符号编码离群值的基本原理上。基于这个基本原理，我们提出了聚类清除，这是一个基于聚类的离群点检测的扩展。这种扩展允许人们评估聚类的代表性，并找到最好由单个独特聚类表示的数据。我们提出了两个有效的算法进行集群清除，一个是无参数，而另一个算法有一个参数，控制代表性估计，允许它在监督设置进行调整。在实验评估中，我们表明，聚类清除提高了从原始聚类中检测到的离群值，并且聚类清除与最先进的替代品竞争激烈。
英文摘要：
Rate-distortion theory-based outlier detection builds upon the rationale that a good data compression will encode outliers with unique symbols. Based on this rationale, we propose Cluster Purging, which is an extension of clustering-based outlier detection. This extension allows one to assess the representivity of clusterings, and to find data that are best represented by individual unique clusters. We propose two efficient algorithms for performing Cluster Purging, one being parameter-free, while the other algorithm has a parameter that controls representivity estimations, allowing it to be tuned in supervised setups. In an experimental evaluation, we show that Cluster Purging improves upon outliers detected from raw clusterings, and that Cluster Purging competes strongly against state-of-the-art alternatives.
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