Principal Cluster Axes: A Projection Pursuit Index for the Preservation of Cluster Structures in the Presence of Data Reduction.
Principal Cluster Axes: A Projection Pursuit Index for the Preservation of Cluster Structures in the Presence of Data Reduction.
复制标题
主聚类轴：在存在数据缩减的情况下保留聚类结构的投影追踪索引。
DOI：
10.1080/00273171.2012.673952
复制
发表时间：
2012
影响因子：
3.8
通讯作者：
Henson,Robert
中科院分区：
文献类型：
--
作者：
Steinley,Douglas;Brusco,MichaelJ;Henson,Robert
“可聚集性”的衡量标准是一种新方法的基础，该方法旨在将集群结构保存在降维空间中。与在多变量空间中寻找最大方差方向的主成分分析类似，主聚类轴在多变量空间中寻找最大聚类性方向。此外，主要的聚类方法属于投影寻踪技术的一类。在模拟研究和对真实世界数据集的分析中，与现有方法进行了比较。此外，通过对最高法院投票数据的分析，演示了如何解释主要群集轴的结果，并提供了对相互竞争程序的解释之间的相似之处(例如，因子分析和主成分分析)。除了最高法院的分析外，我们还分析了几个经常用于测试集群分析过程的数据集，包括Fisher‘s Iris数据、Agreement sti’s Crab数据和一个关于玻璃碎片的数据集。最后，提供了讨论，以帮助确定何时拟议的程序将是最有益的研究人员。
A measure of “clusterability” serves as the basis of a new methodology designed to preserve cluster structure in a reduced dimensional space. Similar to principal component analysis, which finds the direction of maximal variance in multivariate space, principal cluster axes find the direction of maximum clusterability in multivariate space. Furthermore, the principal clustering approach falls into the class of projection pursuit techniques. Comparisons are made with existing methodologies both in a simulation study and analysis of real-world data sets. Furthermore, a demonstration of how to interpret the results of the principal cluster axes is provided on the analysis of Supreme Court voting data and similarities between the interpretation of competing procedures (e.g., factor analysis and principal component analysis) are provided. In addition to the Supreme Court analysis, we analyze several data sets often used to test cluster analysis procedures, including Fisher's Iris data, Agresti's Crab data, and a data set on glass fragments. Finally, discussion is provided to help determine when the proposed procedure will be the most beneficial to the researcher.