中文标题#
無監督在線學習中重疊多變量高斯聚類的度量
英文标题#
Measures of Overlapping Multivariate Gaussian Clusters in Unsupervised Online Learning
中文摘要#
在本文中，我們提出了一種新的度量方法，用於檢測多變量高斯聚類中的重疊。 在線學習從數據流中學習的目的是創建能夠根據流數據的概念漂移隨時間適應的聚類、分類或回歸模型。 在聚類的情況下，這可能導致大量可能重疊的聚類，這些聚類應該被合併。 由於無法考慮所有形狀的聚類以及計算需求高，常用的分佈差異度量在在線學習從流數據中學習的背景下不足以確定重疊聚類。 我們提出的差異度量專門設計用於檢測重疊而不是差異，並且相比現有度量可以更快地計算。 我們的方法比比較方法快幾倍，並且能夠在避免合併正交聚類的同時檢測重疊聚類。
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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