Co-clustering based exploratory analysis of mixed-type data tables

TL;DR

This paper introduces a novel co-clustering method for mixed-type data tables, enabling the discovery of underlying dependency structures between instances and variables of different types through a two-step binarization and clustering process.

Contribution

The paper proposes a new mixed data co-clustering approach that handles both numerical and categorical variables via binarization before applying co-clustering, extending existing techniques.

Findings

Effective on various data sets

Comparable to Multiple Correspondence Analysis

Handles mixed data types efficiently

Abstract

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.