【摘要】 模糊C均值聚类算法(FCM)由于样本模糊隶属度归一性的约束,导致FCM算法对噪声数据敏感。提出松弛模糊C均值聚类算法(RFCM),RFCM算法在可能性C均值聚类算法(PCM)目标函数的基础上,放弃了FCM算法单个样本模糊隶属度归一化约束,转为n个样本模糊隶属度之和为n的约束;并利用粒子群算法对样本模糊隶属度进行优化估计,使得模糊指标可拓展为m>0的情况,同时采用梯度法得到RFCM算法聚类中心迭代公式。RFCM理论分析了算法对噪声数据抗噪的原理,解释了RFCM算法模糊指标m>0的合理性,讨论了RFCM算法的收敛性。基于Gauss数据集和UCI数据集的仿真测试验证了所提出算法的有效性。更多还原

【Abstract】 The FCM algorithm is sensitive to noise data due to the normalized constraint of fuzzy membership. A novel clustering algorithm is proposed and named as relaxed fuzzy C-means clustering( RFCM),the objective function of PCM is utilized as the objective function of RFCM,and RFCM loosens the normalized constraint and only requests the whole summation of n samples’ fuzzy memberships equal to n,particle swarm optimization algorithms( PSO) are optimally used to select the fuzzy memberships of RFCM,and the value scope of fuzzy index m is extended to m > 0,The iterative formula of clustering centers are derived by gradient method for RFCM. The antinoise performance of RFCM is analysed theoretically,and the rationality of new value scope of m > 0 is explained for RFCM,and the convergence of RFCM is discussed simultaneously. The effectiveness of RFCM are proved through simulation experiments.更多还原