K-means properties on six clustering benchmark datasets

被引：0
|
作者：
Pasi Fränti
Sami Sieranoja
机构：
[1] University of Eastern Finland,Machine Learning Group, School of Computing
来源：
Applied Intelligence | 2018年 / 48卷
关键词：
Clustering algorithms; Clustering quality; k-means; Benchmark;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
This paper has two contributions. First, we introduce a clustering basic benchmark. Second, we study the performance of k-means using this benchmark. Specifically, we measure how the performance depends on four factors: (1) overlap of clusters, (2) number of clusters, (3) dimensionality, and (4) unbalance of cluster sizes. The results show that overlap is critical, and that k-means starts to work effectively when the overlap reaches 4% level.
引用
收藏
页码：4743 / 4759
页数：16
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