An LVQ clustering algorithm based on neighborhood granules

被引:6
|
作者
Jiang, Hailiang [1 ]
Chen, Yumin [1 ]
Kong, Liru [1 ]
Cai, Guoqiang [1 ]
Jiang, Hongbo [2 ]
机构
[1] Xiamen Univ Technol, Coll Comp & Informat Engn, Xiamen 361024, Peoples R China
[2] Xiamen Univ Technol, Coll Econ & Management, Xiamen, Peoples R China
基金
中国国家自然科学基金;
关键词
Supervised learning; granular computing; LVQ clustering; neighborhood granules; INFORMATION GRANULATION; CLASSIFICATION; ACCURACY;
D O I
10.3233/JIFS-220092
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Learning Vector Quantization (LVQ) is a clustering method with supervised information, simple structures, and powerful functions. LVQ assumes that the data samples are labeled, and the learning process uses labels to assist clustering. However, the LVQ is sensitive to initial values, resulting in a poor clustering effect. To overcome these shortcomings, a granular LVQ clustering algorithm is proposed by adopting the neighborhood granulation technology and the LVQ. Firstly, the neighborhood granulation is carried out on some features of a sample of the data set, then a neighborhood granular vector is formed. Furthermore, the size and operations of neighborhood granular vectors are defined, and the relative and absolute granular distances between granular vectors are proposed. Finally, these granular distances are proved to be metrics, and a granular LVQ clustering algorithm is designed. Some experiments are tested on several UCI data sets, and the results show that the granular LVQ clustering is better than the traditional LVQ clustering under suitable neighborhood parameters and distance measurement.
引用
收藏
页码:6109 / 6122
页数:14
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