中文标题#
无监督在线学习中重叠多变量高斯聚类的度量
英文标题#
Measures of Overlapping Multivariate Gaussian Clusters in Unsupervised Online Learning
中文摘要#
在本文中，我们提出了一种新的度量方法，用于检测多变量高斯聚类中的重叠。 在线学习从数据流中学习的目的是创建能够根据流数据的概念漂移随时间适应的聚类、分类或回归模型。 在聚类的情况下，这可能导致大量可能重叠的聚类，这些聚类应该被合并。 由于无法考虑所有形状的聚类以及计算需求高，常用的分布差异度量在在线学习从流数据中学习的背景下不足以确定重叠聚类。 我们提出的差异度量专门设计用于检测重叠而不是差异，并且相比现有度量可以更快地计算。 我们的方法比比较方法快几倍，并且能够在避免合并正交聚类的同时检测重叠聚类。
英文摘要#
In this paper, we propose a new measure for detecting overlap in multivariate Gaussian clusters. The aim of online learning from data streams is to create clustering, classification, or regression models that can adapt over time based on the conceptual drift of streaming data. In the case of clustering, this can result in a large number of clusters that may overlap and should be merged. Commonly used distribution dissimilarity measures are not adequate for determining overlapping clusters in the context of online learning from streaming data due to their inability to account for all shapes of clusters and their high computational demands. Our proposed dissimilarity measure is specifically designed to detect overlap rather than dissimilarity and can be computed faster compared to existing measures. Our method is several times faster than compared methods and is capable of detecting overlapping clusters while avoiding the merging of orthogonal clusters.
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