Steinley Douglas
Department of Psychological Sciences, 210 McAlester Hall, Columbia, MO 65211, USA.
Br J Math Stat Psychol. 2008 Nov;61(Pt 2):255-73. doi: 10.1348/000711007X184849. Epub 2007 Feb 22.
This paper develops a new procedure, called stability analysis, for K-means clustering. Instead of ignoring local optima and only considering the best solution found, this procedure takes advantage of additional information from a K-means cluster analysis. The information from the locally optimal solutions is collected in an object by object co-occurrence matrix. The co-occurrence matrix is clustered and subsequently reordered by a steepest ascent quadratic assignment procedure to aid visual interpretation of the multidimensional cluster structure. Subsequently, measures are developed to determine the overall structure of a data set, the number of clusters and the multidimensional relationships between the clusters.
本文针对K均值聚类开发了一种名为稳定性分析的新方法。该方法并非忽略局部最优解而仅考虑找到的最佳解决方案，而是利用K均值聚类分析中的额外信息。来自局部最优解的信息收集在一个逐个对象的共现矩阵中。对该共现矩阵进行聚类，随后通过最速上升二次分配程序重新排序，以辅助对多维聚类结构进行可视化解释。随后，开发了一些度量来确定数据集的整体结构、聚类数量以及聚类之间的多维关系。
