收缩k均值:一种基于James-Stein估计的聚类算法

2016 5th Brazilian Conference on Intelligent Systems (BRACIS) Pub Date : 2016-10-01 DOI:10.1109/BRACIS.2016.084

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。

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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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2016 5th Brazilian Conference on Intelligent Systems (BRACIS)

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