Application of Quantum-behaved Particle Swarm Optimization in projection pursuit clustering

doi:10.3778/j.issn.1002-8331.2011.02.057

Computer Engineering and Applications ›› 2011, Vol. 47 ›› Issue (2): 190-193.DOI: 10.3778/j.issn.1002-8331.2011.02.057

• 图形、图像、模式识别 • Previous Articles Next Articles

Application of Quantum-behaved Particle Swarm Optimization in projection pursuit clustering

ZHANG Qun，LEI Xiujuan，MA Qianzhi

School of Computer Science，Shaanxi Normal University，Xi’an 710062，China

Received:2009-06-10 Revised:2009-09-09 Online:2011-01-11 Published:2011-01-11
Contact: ZHANG Qun

量子粒子群在投影寻踪聚类中的应用

张群，雷秀娟，马千知

陕西师范大学计算机科学学院，西安 710062

通讯作者: 张群

Abstract

Abstract: For solving the large calculation problem to high-dimensional data cluster，a novel projection pursuit cluster based on Quantum-behaved Particle Swarm Optimization（QPSO） algorithm which combines the global searching ability of QPSO and the dimension reduction capability of projection pursuit method are proposed in this paper.Compared with other algorithms，the simulation results of three kinds of testing data show that the proposed algorithm is much more efficient and accurate.

Key words: Quantum-behaved Particle Swarm Optimization（QPSO）, projection pursuit, clustering

摘要： 提出基于量子粒子群的投影寻踪聚类算法，该算法将量子粒子群的全局搜索能力与投影寻踪对高维数据的降维能力相结合，有效解决了高维数据聚类计算量大效率低的问题。并将该算法应用于三种不同的测试数据，仿真实验结果表明该算法具有更好的效率，且提高了聚类效果，是解决高维聚类问题的一种有效方法。

关键词: 量子粒子群优化算法, 投影寻踪, 聚类

CLC Number:

TP391

ZHANG Qun，LEI Xiujuan，MA Qianzhi. Application of Quantum-behaved Particle Swarm Optimization in projection pursuit clustering[J]. Computer Engineering and Applications, 2011, 47(2): 190-193.

张群，雷秀娟，马千知. 量子粒子群在投影寻踪聚类中的应用[J]. 计算机工程与应用, 2011, 47(2): 190-193.

[1]	LAN Hong, HUANG Min. Fusion of KNN Optimized Density Peaks and FCM Clustering Algorithm [J]. Computer Engineering and Applications, 2021, 57(9): 81-88.
[2]	GUO Xiaojing, SUI Haoda. Application of Improved YOLOv3 in Foreign Object Debris Target Detection on Airfield Pavement [J]. Computer Engineering and Applications, 2021, 57(8): 249-255.
[3]	LI Li, JI Xinyuan, SONG Song. Prediction Model for Number of Software Defects in Loop [J]. Computer Engineering and Applications, 2021, 57(7): 158-163.
[4]	YANG Fang, YIN Xi, SI Jianhui, LIU Hongyuan, WANG Xue. Mathematical Expression Similarity Calculation Method Based on Focus Clustering [J]. Computer Engineering and Applications, 2021, 57(6): 88-93.
[5]	ZHAO Fan, ZHANG Lin, WEN Zhiquan, YANG Linlin, LIN Guangfeng. Direct and Efficient Natural Scene Chinese Character Approaching Spotting Method [J]. Computer Engineering and Applications, 2021, 57(6): 159-167.
[6]	HUO Guangyu, ZHANG Yong, SUN Yanfeng, YIN Baocai. Research on Archive Data Intelligent Classification Based on Semantic [J]. Computer Engineering and Applications, 2021, 57(6): 247-253.
[7]	PENG Qihui, XUAN Shibin, GAO Qing. Distribution Automatic Threshold Density Peak Clustering Algorithm [J]. Computer Engineering and Applications, 2021, 57(5): 71-78.
[8]	LI Yongzhen, LIAO Husheng. Multi-view Clustering via Graph Convolutional Neural Network [J]. Computer Engineering and Applications, 2021, 57(5): 115-122.
[9]	WANG Changlong, ZHANG Yuandong, MIAO Hong, YANG Yuheng. Application of Double Channel Convolutional Neural Network in Pumpkin Diseases Identification [J]. Computer Engineering and Applications, 2021, 57(5): 183-189.
[10]	HU Xiaomin, WANG Mingfeng, ZHANG Shourong, LI Min. New Differential Evolution with Particle Swarm Optimization Algorithm for Text Clustering [J]. Computer Engineering and Applications, 2021, 57(4): 61-67.
[11]	WANG Junling, LU Xinming. Video Key Frame Extraction Algorithm Based on Semantic Correlation [J]. Computer Engineering and Applications, 2021, 57(4): 192-198.
[12]	WANG Fuyin, ZHANG Desheng, ZHANG Xiao. Adaptive Density Peaks Clustering Algorithm Combining with Whale Optimization Algorithm [J]. Computer Engineering and Applications, 2021, 57(3): 94-102.
[13]	CHEN Junfeng, ZHENG Zhongtuan. Over-Sampling Method on Imbalanced Data Based on WKMeans and SMOTE [J]. Computer Engineering and Applications, 2021, 57(23): 106-112.
[14]	ZHANG Zhonglin, ZHAO Yu, YAN Guanghui. Natural Neighbor Density Extremum Clustering Algorithm [J]. Computer Engineering and Applications, 2021, 57(23): 200-210.
[15]	MEI Jie, WEI Yuanyuan, XU Taosheng. Fusion Clustering Algorithm Based on Multi-Prototypes Using Density Peaks [J]. Computer Engineering and Applications, 2021, 57(22): 78-85.

Application of Quantum-behaved Particle Swarm Optimization in projection pursuit clustering

量子粒子群在投影寻踪聚类中的应用

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics