SEMI OpenIR  > 半导体人工神经网络实验室
基于特征分析的粒子群优化聚类算法
邓貌; 鲁华祥; 金小贤
2010
Source Publication计算机工程
Volume36Issue:8Pages:185-187
Abstract为提高粒子群优化聚类算法的性能,结合特征分析相关方法,提出一种新的串联聚类算法KPCA-PSO,阐述算法的基本原理和实施方案.在特征分析过程中,以一种简单有效的特征值选择方法避免手动选择特征值的繁琐过程.以人工数据和实际数据测试算法性能,实验结果表明该方法具有较好的聚类效果.
metadata_83半导体人工神经网络实验室
Subject Area人工智能
Funding Organization国家自然科学基金资助项目,国家"863"计划基金资助项目
Indexed ByCSCD
Language中文
CSCD IDCSCD:3860648
Date Available2011-08-16
Citation statistics
Cited Times:1[CSCD]   [CSCD Record]
Document Type期刊论文
Identifierhttp://ir.semi.ac.cn/handle/172111/21742
Collection半导体人工神经网络实验室
Recommended Citation
GB/T 7714
邓貌,鲁华祥,金小贤. 基于特征分析的粒子群优化聚类算法[J]. 计算机工程,2010,36(8):185-187.
APA 邓貌,鲁华祥,&金小贤.(2010).基于特征分析的粒子群优化聚类算法.计算机工程,36(8),185-187.
MLA 邓貌,et al."基于特征分析的粒子群优化聚类算法".计算机工程 36.8(2010):185-187.
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