基于粒子群优化与模糊聚类的社区发现算法

针对现有基于改进的K-means模糊聚类的社区发现算法(k-means algorithm for community structures detection based on fuzzy clustering,NKFCM)执行效率较差的问题,将粒子群算法与模糊聚类算法相结合提出了基于粒子群优化与模糊聚类的社区发现算法(community detection algorithm based on particle swarm optimization and fuzzy clustering,PFCM).该算法首先进行迭代运算,找出初始聚类核心,利用以云模型为运行条件的粒子群优化算法确定最优聚类核心与最佳社区个数,最后利用模糊聚类算法(fuzzy c-means algorithm,FCM)进行具体的社区划分.理论解析与测试结果表明:该算法发现网络社区的准确性较高,且与NKFCM算法相比,PFCM在处理网络数据时执行效率获得了极大地提升.

Community detection algorithm based on particle swarm optimization and fuzzy clustering

In order to improve the efficiency executive of the algorithm, which is based on the optimized algorithm of K- means fuzzy clustering( NKFCM), another algorithm based on particle swarm optimization and fuzzy clustering(PFCM) is proposed. First of all, an iterative calculation is led to get initial community cores. Secondly, we determine the best core of cluster and the optimal number of community with particle swarm algorithm ,which is operated under the conditions of the cloud model. Lastly, we divide communities with fuzzy c-means algorithm (FCM). According to the theoretical analysis and experimental results, it shows that the algorithm is accurate in finding communities of networks and the efficiency of PFCM in dealing with network data improves substantially, compared to NKFCM.