Abstract
In data analytics, to extract features from multivariate data, objects and variables are often displayed in a reduced space that is easy to interpret. When clustering is used in these displays, we often obtain the cluster structure of the original data using an approach combining two multivariate analysis methods called “simultaneous analysis.” Stratifying objects can also extract features of the variables that we would like to interpret. Simultaneous analysis methods for these tasks estimate the unknown parameters of the two methods simultaneously and can find a low-dimensional subspace that reflects the cluster structure. However, despite the many common parts of these methods, it is necessary to change the method depending to the aim of the analysis and data type, making them inconvenient to actually use. To address this shortcoming, we propose a simultaneous analysis framework that is composed of several possible reduction methods integrated with clustering methods. The unified framework is applicable to numerical, categorical, and mixed data. Using this method, we can display objects and variables in a low-dimensional subspace that reflects the cluster structure. Moreover, we discuss the framework’s extensions and how it relates to several other proposed simultaneous analysis methods.
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Mitsuhiro, M., Yadohisa, H. A unified representation of simultaneous analysis methods of reduction and clustering. Jpn J Stat Data Sci 1, 393–412 (2018). https://doi.org/10.1007/s42081-018-0022-6
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DOI: https://doi.org/10.1007/s42081-018-0022-6