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Similarity Measures for Categorical Data: A Comparative Evaluation

9 years 3 months ago
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 discovery tasks. The notion of similarity for continuous data is relatively well-understood, but for categorical data, the similarity computation is not straightforward. Several 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 specific data mining task: outlier detection. Results on a variety of data sets show that while no one measure dominates others for all types of problems, some measures are able to have consistently high performance.
Shyam Boriah, Varun Chandola, Vipin Kumar
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2008
Where SDM
Authors Shyam Boriah, Varun Chandola, Vipin Kumar
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