期刊
APPLIED INTELLIGENCE
卷 48, 期 12, 页码 4743-4759出版社
SPRINGER
DOI: 10.1007/s10489-018-1238-7
关键词
Clustering algorithms; Clustering quality; k-means; Benchmark
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.
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