基于多分辨率感知分组的无监督聚类

T. Syeda-Mahmood, Fei Wang
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引用次数: 5

摘要

在许多实际应用中,聚类是一种常见的数据分区操作。通常,这样的数据分布表现出对问题表征很重要的高级结构,但现有的聚类算法没有明确地发现这些结构。本文引入多分辨率感知分组作为一种无监督聚类方法。具体来说,我们使用了接近性、密度、邻近性和方向相似性的感知分组约束。我们以多分辨率的方式应用这些约束,将高维空间中的样本点分组为显著簇。我们对大型数据集上最先进的监督和无监督聚类方法的聚类算法进行了广泛的评估。
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Unsupervised Clustering using Multi-Resolution Perceptual Grouping
Clustering is a common operation for data partitioning in many practical applications. Often, such data distributions exhibit higher level structures which are important for problem characterization, but are not explicitly discovered by existing clustering algorithms. In this paper, we introduce multi-resolution perceptual grouping as an approach to unsupervised clustering. Specifically, we use the perceptual grouping constraints of proximity, density, contiguity and orientation similarity. We apply these constraints in a multi-resolution fashion, to group sample points in high dimensional spaces into salient clusters. We present an extensive evaluation of the clustering algorithm against state-of-the-art supervised and unsupervised clustering methods on large datasets.
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