CABOSFV算法中集合稀疏差异度阈值确定方法

The method of how to determine threshold value of set-square-difference in CABOSFV algorithm

在实际应用中,CABOSFV算法初始参数———集合稀疏差异度阈值b的确定是否合理,对聚类结果是否有效起决定作用。本文针对如何科学方便地确定集合稀疏差异度阈值b进行了深入研究,给出了集合稀疏差异度阈值确定方法,并通过该方法进行了实例计算。计算结果表明,由于该方法能够确定聚类结果中类的对象组成最小数量,聚类结果的粗糙与精细程度可以人为控制,对聚类结果的准确及高效提供了很好的保证,能够为CABOSFV算法进行聚类提供合理的阈值。

Using CABOFSV to cluster, whether b , the beginning parameter, threshold value of setsquare-difference, also named up-bound of a cluster, is reasonable or not is fatal to clustering results. In this paper, how to determine the threshold value of set-square-difference in CABOSFV algorithm is deeply studied. Then, the method of how to determine threshold value of set-square-difference is put forward and is expressed by a formula, and a group of data is calculated by this method. The calculating results indicate that this threshold is reasonable for CABOSFV because clustering results are controlled by people and ensured correctly and effectively for this method can fix the least number of objects in one cluster.