| 基于改进差分进化的K-均值聚类算法 |
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| 引用本文： | 高平,毛力,宋益春.基于改进差分进化的K-均值聚类算法[J].数字社区&智能家居,2013(8):5064-5067. |
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| 作者姓名： | 高平 毛力 宋益春 |
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| 作者单位： | [1]无锡信捷电气股份有限公司,江苏无锡214072 [2]轻工过程先进控制教育部重点实验室,江南大学物联网工程学院,江苏无锡214122 |
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| 基金项目： | 轻工过程先进控制教育部重点实验室开放课题资助（江南大学）项目（APCLI1004） |
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| 摘 要： | 针对K-均值算法对初始值敏感和易陷入局部最优的缺点,提出了一种基于改进差分进化的K-均值聚类算法。该算法通过引入基于Laplace分布的变异算子和Logistic变尺度混沌搜索来增强全局寻优能力。实验结果表明,该算法能够较好地克服传统K-均值算法的缺点,具有较好的搜索能力,且算法的收敛速度较快,鲁棒性较强。
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| 关 键 词： | 聚类分析 差分进化 K-均值聚类算法 Laplace分布 Logistic混沌搜索 |
A K-means Clustering Algorithm Based on Enhanced Differential Evolution |
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| GAO Ping,MAO Li,SONG Yi-chun.A K-means Clustering Algorithm Based on Enhanced Differential Evolution[J].Digital Community & Smart Home,2013(8):5064-5067. |
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| Authors: | GAO Ping MAO Li SONG Yi-chun |
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| Affiliation: | 1 Xinje Electronic Co., Ltd. , Wuxi 214000, China; 2 .Key Laboratory of Advanced Process Control for Light Industry (Minis- try of Education), School of Internet of Things, Jiangnan University, Wuxi 214122, China) |
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| Abstract: | The conventional k-means algorithms are sensitive to the initial cluster centers, and tend to be trapped by local opti- ma. To resolve these problems, a novel k-means clustering algorithm using enhanced differential evolution technique is proposed in this paper. This algorithm improves the global search ability by applying Laplace mutation operator and variable-scale Logistic chaotic searching. Numerical experiments show that this algorithm overcomes the disadvantages of the conventional k-means al- gorithms, and improves search ability with higher accuracy, faster convergence speed and better robustness. |
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| Keywords: | cluster analysis differential evolution k-means cluster algorithm Laplace distribution Logistic chaotic searching |
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