However, probability-based clustering algorithms do not directly answer the question,how many clusters in a given data set?
但是,如何在给定的数据集上得到最佳聚类个数? 概率聚类算法本身并没有回答这个问题。
参考来源 - 聚类分析算法研究·2,447,543篇论文数据，部分数据来源于NoteExpress
实验表明，EPFCM算法可以有效地得到最佳的类中心个数，聚类结果不受初始类中心影响，并且陷入局部极小的概率较FCM算法大大降低。
Experiments show that EPFCM algorithm can gain best cluster centers and optimal cluster structures, and the probability of falling into local minima is greatly reduced.
同时改进了现有的直觉模糊聚类算法中的概率型约束条件，使其对噪声和野值点具有较好的鲁棒性。
Besides, it was robust to the noises because it improved the constraint conditions used in the existing intuitionistic fuzzy clustering algorithm.
应用推荐