基于IGS方法的最佳分类数研究*

针对选择Gap Statistic(GS)方法估计聚类数能够得到数据集的粗略分类,但不能进一步对数据集进行细分类这一问题,对GS方法进行改进;将Gap统计量引入到ISODATA算法中,提出了IGS模型;实证表明,IGS模型不仅可以实现数据的细分类,而且通过IGS模型估计数据集的最佳分类数准确率明显高于原GS模型。

Research on the Optimal Number of Classification Based on IGS Method

According to the problem that Gap Statistics (GS) method can get rough classification of data sets and can not get the fine classification of data sets, this paper improves GS method by introducing Gap statistic into ISODATA algorithm and proposes IGS model. Empirical research indicates that IGS model can not only realize the fine classification of data but also can estimate the optimal number of classification of the data sets through IGS model whose accuracy is obviously higher than original GS model.