一种基于隶属度优化的演化聚类算法

A Membership Degree Refinement-Based Evolutionary Clustering Algorithm

针对FCM中数据点隶属度的计算是影响算法执行效率的主要因素,提出一种新的加速FCM算法(accelerated fuzzy C-means,AFCM),用于加速FCM及基于FCM的演化聚类算法.AFCM算法采用抽样初始化操作,产生较好的初始聚类中心,对于拥有较大隶属度的数据点,通过一步k-means操作更新模糊聚类中心,同时仅更新小隶属度来达到加速FCM算法的目的.为了验证所提出方法的有效性并提高聚类算法的效率,将AFCM应用于基于演化算法的模糊聚类算法.实验表明,此方法在保持良好的聚类结果前提下,能够减少大规模数据集上聚类算法的计算时间.

Though FCM has already been widely used in clustering, its alternative calculation of the membership and prototype matrix causes a computational burden for large-scale data sets. An efficient algorithm, called accelerated fuzzy C-means （AFCM）, is presented for reducing the computation time of FCM and FCM-based clustering algorithms. The proposed algorithm works by sampling initiation to generate better initial cluster centers, and motivated by the observation that there is the increasing trend for large membership degree values of data points at next iteration, updating cluster center using one step k-means for those data points with large membership degree values and only updating membership of data points with small values at next iteration. To verify the effectiveness of the proposed algorithm and improve the efficiency of EA for fuzzy clustering, AFCM also is applied to fuzzy clustering algorithms based on EAs, such as differential evolution （DE） and evolutionary programming （EP）, to observe their performances. Experiments indicate that with a small loss of quality, the proposed algorithm can significantly reduce the computation time of clustering.