A Framework for Clustering Mixed Attribute Type Datasets

We propose a clustering framework that supports clustering of datasets with mixed attribute type (numerical, categorical), while minimizing information loss during clustering. Real world datasets such as medical datasets and its ontology have mixed attribute type datasets. However, most conventional clustering algorithms have been designed and applied to datasets containing only single attribute type (either numerical or categorical). Recently, approaches to clustering for mixed attribute type datasets have emerged, but they are mainly based on transforming attributes to straightforwardly utilize conventional algorithms. The problem of such approaches is the possibility of distorted results due to the loss of information because significant portion of attribute values can be removed in the transforming process. This results in a lower accuracy clustering. To address this problem, we propose a clustering framework for mixed attribute type datasets without transforming attributes. We first utilize an entropy based measure of categorical attributes as our criterion function for similarity. Second, based on the results of entropy based similarity, we extract candidate cluster numbers and verify our weighting scheme with pre-clustering results. Finally, we cluster the mixed attribute type datasets with the extracted candidate cluster numbers and the weights. Our experimental results demonstrate that the proposed framework is effective in increasing accuracy.