Similarity Measures for Categorical Data: A Comparative Evaluation

Measuring similarity or distance between two entities is a key step for several data mining and knowledge discov- ery tasks. The notion of similarity for continuous data is relatively well-understood, but for categorical data, the similarity computation is not straightforward. Sev- eral data-driven similarity measures have been proposed in the literature to compute the similarity between two categorical data instances but their relative performance has not been evaluated. In this paper we study the performance of a variety of similarity measures in the context of a specic data mining task: outlier detec- tion. Results on a variety of data sets show that while no one measure dominates others for all types of prob- lems, some measures are able to have consistently high performance.