摘要
针对传统模糊C均值(FCM)算法在聚类过程中存在收敛速度慢、对大数据处理实时性不强等问题,提出了一种基于惩罚因子的样本隶属度改进算法。首先分析抑制式模糊C均值(SFCM)聚类特点,研究惩罚因子对样本隶属度修正的触发条件,进而设计出基于惩罚因子的SFCM聚类隶属度动态修正算法。通过算法实现样本向"两极移动",达到快速收敛之目的。理论分析与实验结果表明,在相同的初始化条件下,改进算法的执行时间效率比传统FCM算法提高约40%,比基于优化选择的SFCM(OS-SFCM)算法提高10%,其聚类准确度与其他两种算法相比也有一定的提高。
Aiming at the problem of slow convergence and weak real-time processing of large data in general Fuzzy C- Means （FCM） algorithm, an improved method of penalty factor on sample membership was proposed. Firstly, the characteristics of Suppressed Fuzzy C-Means （SFCM） clustering were analyzed, and the trigger condition for adjusting sample membership by penalty factor was studied, and then the dynamic membership adjusting scheme of SFCM based on penalty factor was designed. By using the algorithm, the samples are ＂moved to the poles＂ to achieve the purpose of rapid convergence. Theoretical analysis and experimental result show that under the same initial condition, the execution time efficiency of the improved algorithm is increased by 40% and 10% respectively compared with the traditional FCM and 0ptimal-Selection-based SFCM （OS-SFCM）, at the same time, the clustering accuracy is also improved.
出处
《计算机应用》
CSCD
北大核心
2016年第9期2427-2431,共5页
journal of Computer Applications
基金
湖南省自然科学基金资助项目(2015JJ2047)
湖南省教育厅项目(13C032)
湖南工业大学科研项目(2014HXZ28)
关键词
抑制式模糊C均值
惩罚因子
模糊隶属度
快速收敛
Suppressed Fuzzy C-Means （SFCM）
penalty factor
fuzzy membership
fast convergence
