Similarity measures for categorical data: A comparative evaluation

Shyam Boriah, Varun Chandola, Vipin Kumar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

298 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationSociety for Industrial and Applied Mathematics - 8th SIAM International Conference on Data Mining 2008, Proceedings in Applied Mathematics 130
Pages243-254
Number of pages12
StatePublished - Oct 1 2008
Event8th SIAM International Conference on Data Mining 2008, Applied Mathematics 130 - Atlanta, GA, United States
Duration: Apr 24 2008Apr 26 2008

Publication series

NameSociety for Industrial and Applied Mathematics - 8th SIAM International Conference on Data Mining 2008, Proceedings in Applied Mathematics 130
Volume1

Other

Other8th SIAM International Conference on Data Mining 2008, Applied Mathematics 130
CountryUnited States
CityAtlanta, GA
Period4/24/084/26/08

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