Shrinkage k-Means: A Clustering Algorithm Based on the James-Stein Estimator

Filipe F. R. Damasceno,Marcelo B. A. Veras,Diego Parente Paiva Mesquita,João Paulo Pordeus Gomes,Carlos Eduardo Fisch de Brito

2016 5th Brazilian Conference on Intelligent Systems (BRACIS)（2016）

引用 3|浏览1

暂无评分

摘要

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.

查看译文

关键词

shrinkage k-means,real-world data,synthetic data,multivariate normal,Sk-means,James-Stein estimator,clustering algorithm

AI 理解论文

溯源树

样例

生成溯源树，研究论文发展脉络

Chat Paper

正在生成论文摘要