K-means Clustering Based on Improved Quantum Particle Swarm Optimization Algorithm

被引：2
|
作者：
Bai, Lili [1 ]
Song, Zerui [1 ]
Bao, Haijie [2 ]
Jiang, Jingqing [1 ]
机构：
[1] Inner Mongolia Univ Nationalities, Coll Comp Sci & Technol, Tongliao, Peoples R China
[2] Inner Mongolia Univ Nationalities, Coll Math & Phys, Tongliao, Peoples R China
来源：
2021 13TH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTATIONAL INTELLIGENCE (ICACI) | 2021年
基金：
中国国家自然科学基金;
关键词：
K-means clustering algorithm; Quantum Particle Swarm Optimization algorithm; QPSO-K-means algorithm; Cloning; Mutation;
D O I：
10.1109/ICACI52617.2021.9435862
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In clustering, in order to find a better data clustering center, make the algorithm convergence faster and clustering results more accurate, a k-means clustering algorithm based on improved quantum particle swarm optimization algorithm is proposed. In this algorithm, the cluster center is simulated as a particle. Cloning and mutation operations are used to increase the diversity and improve the global search ability of QPSO. A suitable and stable cluster center is obtained. Finally, an effective clustering result is obtained. The algorithm is tested with UCI data set. The results show that the improved algorithm not only ensures the global convergence of the algorithm, but also obtains more accurate clustering results.
引用
收藏
页码：140 / 145
页数：6
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