Clustering Mixed Datasets Using Homogeneity Analysis with Applications to Big Data
Machine Learning
2017-10-31 v3
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.
Cite
@article{arxiv.1608.04961,
title = {Clustering Mixed Datasets Using Homogeneity Analysis with Applications to Big Data},
author = {Rajiv Sambasivan and Sourish Das},
journal= {arXiv preprint arXiv:1608.04961},
year = {2017}
}