| 基于APSO的模糊聚类算法 |
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| 引用本文： | 李金霞.基于APSO的模糊聚类算法[J].科学技术与工程,2009,9(19). |
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| 作者姓名： | 李金霞 |
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| 作者单位： | 1. 江苏科技大学计算机科学与工程学院,镇江,212003
2. 江苏科技大学计算机科学与工程学院,镇江,212003;浙江大学CAD&CG国家重点实验室,杭州,310027 |
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| 基金项目： | 江苏省高校自然科学基础研究，浙江大学CAD & CG国家重点实验室开放课题 |
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| 摘 要： | 利用改进的自适应粒子群优化算法(APSO)较强全局寻优、快速收敛的特点和模糊C-均值算法(FCM)对初始值敏感、容易陷入局部最优的缺点.提出一种基于自适应粒子群优化算法的模糊聚类算法(APFM).新算法有效的克服了FCM算法的缺点,同时增强了APSO算法全局搜索和跳出局部最优的能力.实验表明:新算法与单一的FCM和APSO算法相比聚类更准确,效率更高.
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| 关 键 词： | 自适应粒子群优化算法 模糊聚类 模糊C-均值算法 |
| 收稿时间： | 6/17/2009 7:37:59 PM |
| 修稿时间： | 6/17/2009 7:37:59 PM |
Fuzzy clustering algorithm based on APSO |
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| lijinxia.Fuzzy clustering algorithm based on APSO[J].Science Technology and Engineering,2009,9(19). |
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| Authors: | lijinxia |
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| Abstract: | By taking advantage of overall optimization, rapid convergence from improved APSO, and disadvantage of sensitive initial value and get lost into overall optimization easily from fuzzy C-mean algorithm, a new algorithm which is called APFM has been proposed based on APSO. This algorithm has overcome the disadvantage of FCM algorithm effectively; meanwhile, it has enhanced the capability of overall searching and local optimum jumping from APSO algorithm. According to the test, comparing with single FCM and APSO algorithm, this algorithm is more accurate in clustering and higher efficiency as well. |
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| Keywords: | Automatic particle swarm optimization fuzzy clustering fuzzy C- mean algorithm |
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