收缩k均值:一种基于James-Stein估计的聚类算法

Filipe F. R. Damasceno, Marcelo B. A. Veras, D. Mesquita, J. Gomes, Carlos Brito
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引用次数: 4

摘要

在这项工作中,我们提出了收缩k-means (Sk-means),这是基于给定单个样本点的多元正态均值的James-Stein估计量的k-means的新变体。我们在合成数据和真实数据上评估Sk-means。该方法优于标准聚类方法,也优于现有的基于k-means的James-Stein估计方法。结果还表明,sk均值对异常值具有鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Shrinkage k-Means: A Clustering Algorithm Based on the James-Stein Estimator
In this work, we propose Shrinkage k-means (Sk-means), a novel variant of k-means based on the James-Stein estimator for the mean of a multivariate normal given a single sample point. We evaluate Sk-means on both synthetic and real-world data. The proposed method outperformed standard clustering methods and also the existing method based on k-means which uses the James-Stein estimator. Results also suggest that Sk-means is robust to outliers.
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