Similarity Measures for Categorical Data: A Comparative Evaluation. Boriah, S.; Chandola, V.; and Kumar, V. In Proceedings of the 2008 SIAM International Conference on Data Mining, of Proceedings, pages 243–254. Society for Industrial and Applied Mathematics.
Similarity Measures for Categorical Data: A Comparative Evaluation [link]Paper  doi  abstract   bibtex   
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.
@incollection{boriahSimilarityMeasuresCategorical2008,
  title = {Similarity Measures for Categorical Data: A Comparative Evaluation},
  shorttitle = {Similarity {{Measures}} for {{Categorical Data}}},
  booktitle = {Proceedings of the 2008 {{SIAM International Conference}} on {{Data Mining}}},
  author = {Boriah, S. and Chandola, V. and Kumar, V.},
  date = {2008-04-24},
  pages = {243--254},
  publisher = {{Society for Industrial and Applied Mathematics}},
  doi = {10.1137/1.9781611972788.22},
  url = {https://doi.org/10.1137/1.9781611972788.22},
  urldate = {2019-04-30},
  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.},
  isbn = {978-0-89871-654-2},
  keywords = {~INRMM-MiD:z-DP4CKMH4,categorical-variables,comparison,data-transformation-modelling,machine-learning,no-free-lunch-theorem,silver-bullet,similarity},
  series = {Proceedings},
  volumes = {0}
}
Downloads: 0