Weighted adjacent matrix for K-means clustering

被引：5
|
作者：
Zhou, Jukai [1 ]
Liu, Tong [1 ]
Zhu, Jingting [1 ]
机构：
[1] Massey Univ, Sch Nat & Computat Sci, Auckland, New Zealand
来源：
MULTIMEDIA TOOLS AND APPLICATIONS | 2019年 / 78卷 / 23期
关键词：
k-means clustering; Similarity measurement; Adjacent matrix; Unsupervised learning; ALGORITHM;
D O I：
10.1007/s11042-019-08009-x
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
K-means clustering is one of the most popular clustering algorithms and has been embedded in other clustering algorithms, e.g. the last step of spectral clustering. In this paper, we propose two techniques to improve previous k-means clustering algorithm by designing two different adjacent matrices. Extensive experiments on public UCI datasets showed the clustering results of our proposed algorithms significantly outperform three classical clustering algorithms in terms of different evaluation metrics.
引用
收藏
页码：33415 / 33434
页数：20
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