Brusco Michael J, Doreian Patrick, Steinley Douglas
Florida State University, Tallahassee, FL, USA.
Department of Marketing, College of Business, Florida State University, 821 Academic Way, Tallahassee, FL, 32306-1110, USA.
Behav Res Methods. 2016 Jun;48(2):487-502. doi: 10.3758/s13428-015-0587-y.
An asymmetric one-mode data matrix has rows and columns that correspond to the same set of objects. However, the roles of the objects frequently differ for the rows and the columns. For example, in a visual alphabetic confusion matrix from an experimental psychology study, both the rows and columns pertain to letters of the alphabet. Yet the rows correspond to the presented stimulus letter, whereas the columns refer to the letter provided as the response. Other examples abound in psychology, including applications related to interpersonal interactions (friendship, trust, information sharing) in social and developmental psychology, brand switching in consumer psychology, journal citation analysis in any discipline (including quantitative psychology), and free association tasks in any subarea of psychology. When seeking to establish a partition of the objects in such applications, it is overly restrictive to require the partitions of the row and column objects to be identical, or even the numbers of clusters for the row and column objects to be the same. This suggests the need for a biclustering approach that simultaneously establishes separate partitions of the row and column objects. We present and compare several approaches for the biclustering of one-mode matrices using data sets from the empirical literature. A suite of MATLAB m-files for implementing the procedures is provided as a Web supplement with this article.
一个非对称单模数据矩阵的行和列对应于同一组对象。然而，这些对象在行和列中的角色常常不同。例如，在一项实验心理学研究的视觉字母混淆矩阵中，行和列都与字母表中的字母相关。然而，行对应于呈现的刺激字母，而列则指作为反应给出的字母。心理学中还有许多其他例子，包括社会和发展心理学中与人际互动（友谊、信任、信息共享）相关的应用、消费心理学中的品牌转换、任何学科（包括定量心理学）中的期刊引用分析以及心理学任何子领域中的自由联想任务。在这类应用中寻求建立对象的划分时，要求行和列对象的划分相同，甚至要求行和列对象的聚类数量相同，这种限制过于严格。这表明需要一种双聚类方法，同时为行和列对象建立单独的划分。我们使用来自实证文献的数据集，展示并比较了几种单模矩阵双聚类的方法。本文提供了一套用于实现这些程序的MATLAB m文件作为网络补充材料。
