Clustering Mixed Datasets Using Homogeneity Analysis with Applications to Big Data

TL;DR

This paper explores using homogeneity analysis to cluster datasets with mixed numerical and categorical data, enabling the application of Euclidean-based tools for big data analysis.

Contribution

It introduces a method to represent mixed datasets in Euclidean space via homogeneity analysis, facilitating clustering and analysis.

Findings

Effective clustering of mixed datasets demonstrated

Applicable to large-scale big data scenarios

Enables use of Euclidean tools for mixed data analysis

Abstract

Datasets with a mixture of numerical and categorical attributes are routinely encountered in many application domains. In this work we examine an approach to clustering such datasets using homogeneity analysis. Homogeneity analysis determines a euclidean representation of the data. This can be analyzed by leveraging the large body of tools and techniques for data with a euclidean representation. Experiments conducted as part of this study suggest that this approach can be useful in the analysis and exploration of big datasets with a mixture of numerical and categorical attributes.