| 基于改进QPSO的模糊C-均值聚类算法 |
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| 引用本文： | 杨照峰,时合生.基于改进QPSO的模糊C-均值聚类算法[J].现代电子技术,2014(7):118-120. |
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| 作者姓名： | 杨照峰 时合生 |
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| 作者单位： | [1]平顶山学院软件学院,河南平顶山467002 [2]平顶山学院计算机科学与技术学院,河南平顶山467002 |
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| 基金项目： | 河南省科技计划重点项目资助（102102210416） |
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| 摘 要： | 针对模糊C-均值聚类算法容易陷入局部极值等缺陷,提出了基于改进QPSO的模糊C-均值聚类,算法利用QPSO的优点,并对量子门更新策略进行了改进。实验结果显示该算法提高了模糊聚类算法的聚类效果以及搜索能力,在全局寻优能力、跳出局部最优能力、收敛速度等方面具有优势。
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| 关 键 词： | 模糊C-均值聚类 量子粒子群优化 聚类分析 量子门更新策略 |
Fuzzy C-means clustering algorithm based on improved QPSO |
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| YANG Zhao-feng,SHI He-sheng.Fuzzy C-means clustering algorithm based on improved QPSO[J].Modern Electronic Technique,2014(7):118-120. |
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| Authors: | YANG Zhao-feng SHI He-sheng |
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| Affiliation: | 1. Software Engineering School,Pingdingshan University,Pingdingshan 467002,China;2. Computer Science and Technical College,Pingdingshan University,Pingdingshan 467002,China) |
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| Abstract: | Since the fuzzy C-means clustering algorithm is easy to fall into local extremum,fuzzy C-means clustering algo-rithm based on the improved quantum particle swarm optimization （QPSO） is proposed. The local search ability and quantum gates update strategy were improved by making full use of the advantages of fast convergence of QPSO. The experimental results show that the algorithm improves the search ability and clustering effect of fuzzy clustering algorithm,and has superiority in the aspects of global optimization capability,jumping out of local optimum capacity and convergence rate. |
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| Keywords: | fuzzy C-means clustering quantum particle swarm optimization clustering analysis quantum gates update strategy |
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