Clustering Mixed Datasets Using Homogeneity Analysis with Applications to Big Data

Rajiv Sambasivan; Sourish Das

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arXiv · 1608.04961

Clustering Mixed Datasets Using Homogeneity Analysis with Applications to Big Data

Rajiv Sambasivan·Sourish Das

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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.

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BibTeX RIS

Rajiv Sambasivan, Sourish Das. 2016-08-17. Clustering Mixed Datasets Using Homogeneity Analysis with Applications to Big Data. https://arxiv.org/abs/1608.04961

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