集粒度计算、蚁群算法与模糊思想的聚类算法

Clustering Algorithm Combined Granular Computing,Ant Colony Algorithm and Fuzzy Idea

模糊C均值聚类算法在开始时采用随机的方式选取初始聚类中心,该方式使得FCM算法对初始聚类中心的选取极为敏感,且在局部范围内较易得到最优解,但是在全局范围内的效果较差;蚁群聚类算法根据先验知识随意设定蚂蚁拾起或放下数据对象的概率,缺乏严密的数学依据。针对FCM算法和蚁群算法的不足,文中将模糊粒度计算的思想推广应用到蚁群聚类算法中,并将改进后的蚁群聚类算法与模糊C均值聚类算法相结合,提出了一种将粒度计算、蚁群算法与模糊C均值算法思想相结合的聚类算法。经过实验验证,改进后的算法较原算法具有更好的聚类效果。

Fuzzy C-means clustering algorithm uses a random manner to select the cluster centers at the beginning, which makes fuzzy C -means clustering algorithm extremely sensitive to the selected initial cluster centers, and it is more easily to get the optimal solution in the local area,but the effect is not very well in the global scope. Ant colony clustering algorithm arbitrarily sets the probability of ants picking up or down the data object according to the priori knowledge, lack of rigorous mathematical basis. Focusing on the shortage of FCM algorithm and ant colony clustering algorithm,in this paper, apply the granular computing to the ant colony clustering algorithm, and combined the improved ant colony clustering algorithm and fuzzy C-means clustering algorithm, propose an improved fuzzy C-means clustering algorithm. Verified by experiments, the improved algorithm is better than the original algorithm on clustering effect.