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Improving hierarchical cluster analysis: A new method with outlier detection and automatic clustering

机译：改进层次聚类分析：具有异常值检测和自动聚类的新方法

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摘要

Techniques based on agglomerative hierarchical clustering constitute one of the most frequent approaches in unsupervised clustering. Some arebased on the single linkage methodology, which has been shown to produce good results with sets of clusters of various sizes and shapes. However, the application of this type of algorithms in a wide variety of fields has posed a number of problems, such as the sensitivity to outliers and fluctuations in the density of data points. Additionally, these algorithms do not usually allow for automatic clustering. In this work we propose a method to improve single linkage hierarchical cluster analysis (HCA), so as to circumvent most of these problems and attain the performance of most sophisticated new approaches. This completely automated method is based on a self-consistent outlier reduction approach, followed by the building-up of a descriptive function. This, in turn, allows to define natural clusters. Finally, the discarded objects may be optionally assigned to these clusters. The validation of the method is carried out by employing widely used data sets available from literature and others for specific purposes created by the authors. Our method is shown to be very efficient in a large variety of situations.
机译：基于聚集层次聚类的技术是无监督聚类中最常见的方法之一。其中一些是基于单链接方法的，该方法已被证明可以在各种大小和形状的群集集合中产生良好的结果。但是，这类算法在广泛领域中的应用带来了许多问题，例如对异常值的敏感性和数据点密度的波动。此外，这些算法通常不允许自动聚类。在这项工作中，我们提出了一种改进单链接层次聚类分析（HCA）的方法，以规避大多数这些问题并获得最复杂的新方法的性能。这种完全自动化的方法基于自洽离群值减少方法，然后建立描述性功能。反过来，这允许定义自然簇。最后，可以将丢弃的对象选择性地分配给这些群集。该方法的验证是通过使用广泛使用的数据集来实现的，这些数据集可以从作者那里获得，也可以用于作者创建的特定目的。我们的方法在很多情况下都非常有效。

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