怎么决定当实现一个特定的聚类算法时,簇的一个适当数字是很重要的,象 c 工具一样,模糊 c 工具(FCM ) 。在文学,大多数聚类正确性索引从分区或数据集合的几何性质被发源。在这篇论文,作者为 FCM 开发了一个新奇聚类正确性索引,基于 FCM 的 theoptimality 测试。不同于以前的聚类正确性索引,这个新奇聚类正确性索引自己在 FCM 是固有的。比较实验证明稳定性指数能为模糊 c 工具被用作聚类正确性索引。
How to determine an appropriate number of clusters is very important when implementing a specific clustering algorithm, like c-means, fuzzy c-means (FCM). In the literature, most cluster validity indices are originated from partition or geometrical property of the data set. In this paper, the authors developed a novel cluster validity index for FCM, based on the optimality test of FCM. Unlike the previous cluster validity indices, this novel cluster validity index is inherent in FCM itself. Comparison experiments show that the stability index can be used as cluster validity index for the fuzzy c-means.