| 基于PSO的模糊K-Prototypes聚类 |
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| 引用本文： | 尹波,何松华.基于PSO的模糊K-Prototypes聚类[J].计算机工程与设计,2008,29(11):2883-2885. |
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| 作者姓名： | 尹波 何松华 |
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| 作者单位： | 湖南大学,计算机与通信学院,湖南,长沙,410082 |
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| 摘 要： | 模糊K-Prototypes(FKP)算法能够对包含数值属性和分类属性相混合的数据集进行有效聚类,但是存在对初始值敏感、容易陷入局部极小值的问题.为了克服该缺点,提出了一种基于粒子群优化(PSO)算法和FKP算法的混合聚类算法,先利用PSO算法确定FKP的初始聚类中心,再将PSO聚类结果作为后续FKP算法的初始值.实验结果表明,新算法具有良好的收敛性和稳定性,聚类效果优于单一使用FKP算法.
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| 关 键 词： | 聚类分析 粒子群优化算法 模糊聚类算法 数值型属性 分类型属性 聚类中心 |
| 文章编号： | 1000-7024(2008)11-2883-03 |
| 修稿时间： | 2007年6月18日 |
Fuzzy K-Prototypes clustering based on particle swarm optimization |
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| YIN Bo,HE Song-hua.Fuzzy K-Prototypes clustering based on particle swarm optimization[J].Computer Engineering and Design,2008,29(11):2883-2885. |
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| Authors: | YIN Bo HE Song-hua |
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| Affiliation: | YIN Bo,HE Song-hua (College of Computer , Communication,Hunan University,Changsha 410082,China) |
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| Abstract: | Fuzzy K-Prototypes (FKP) algorithm is efficient in clustering data sets with mixed numeric and categorical values, with the defects including sensitivity to the initial data and being easy to run into the local optimization. In order to overcome them, a new hybrid clustering algorithm based on particle swarm (PSO) optimization and FKP algorithm is proposed, by using PSO to determine the centroids of clusters and taking the clustering result of PSO as the initialized value of the FKP. The results show that t... |
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| Keywords: | clustering analysis particle swarm optimization fuzzy clustering algorithm numeric attribute categorical attribute cluster-centroids |
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