Co-clustering based exploratory analysis of mixed-type data tables
This addresses the limitation of existing co-clustering techniques that are restricted to same-type variables, offering a solution for exploratory analysis of mixed data, though it appears incremental.
The paper tackles the problem of co-clustering mixed-type data by proposing a two-step method that binarizes variables and applies co-clustering, resulting in groups of instances and variable parts, with comparisons to Multiple Correspondence Analysis on several datasets.
Co-clustering is a class of unsupervised data analysis techniques that extract the existing underlying dependency structure between the instances and variables of a data table as homogeneous blocks. Most of those techniques are limited to variables of the same type. In this paper, we propose a mixed data co-clustering method based on a two-step methodology. In the first step, all the variables are binarized according to a number of bins chosen by the analyst, by equal frequency discretization in the numerical case, or keeping the most frequent values in the categorical case. The second step applies a co-clustering to the instances and the binary variables, leading to groups of instances and groups of variable parts. We apply this methodology on several data sets and compare with the results of a Multiple Correspondence Analysis applied to the same data.