A Kernel K-means Clustering Algorithm Based on an Adaptive Mahalanobis Kernel

被引：0
|
作者：
Ferreira, Marcelo R. P. [1 ]
de Carvalho, Francisco de A. T. [2 ]
机构：
[1] Univ Fed Paraiba, Dept Stat, BR-58059900 Joao Pessoa, Paraiba, Brazil
[2] Univ Fed Pernambuco, Ctr Informat, Recife, PE, Brazil
来源：
PROCEEDINGS OF THE 2014 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2014年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, a kernel K-means algorithm based on an adaptive Mahalanobis kernel is proposed. This kernel is built based on an adaptive quadratic distance defined by a symmetric positive definite matrix that changes at each algorithm iteration and takes into account the correlations between variables, allowing the discovery of clusters with non-hyperspherical shapes. The effectiveness of the proposed algorithm is demonstrated through experiments with synthetic and benchmark datasets.
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页码：1885 / 1892
页数：8
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