%0 Journal Article
%T Clustering Mixed Datasets Using Homogeneity Analysis with Applications to Big Data
%A Rajiv Sambasivan
%A Sourish Das
%J Calcutta Statistical Association Bulletin
%@ 2456-6462
%D 2018
%R 10.1177/0008068318814630
%X Datasets with a mixture of numerical and categorical attributes are routinely encountered in many application domains. Such datasets do not have a direct representation in Euclidean space. As a consequence, dissimilarity measures such as the Gower distance are used when partitioning clustering approaches are used with such datasets. Homogeneity analysis (HA) can be used to determine a Euclidean representation of mixed datasets. Such a representation can be analysed by leveraging the large body of tools and techniques for data with a Euclidean representation. The utility of the representation obtained from HA is not limited to clustering. This representation can be used to visualize mixed datasets and generate succinct numerical summaries. Such summaries can yield clues about associations between variables which may be difficult to discover otherwise. AMS Classification Code: 62-0
%K Mixed datasets
%K Clustering
%K big data
%U https://journals.sagepub.com/doi/full/10.1177/0008068318814630