Mining Distance-Based Outliers from Categorical Data

@article{Li2007MiningDO,
  title={Mining Distance-Based Outliers from Categorical Data},
  author={Shuxin Li and Robert Lee and Sheau-Dong Lang},
  journal={Seventh IEEE International Conference on Data Mining Workshops (ICDMW 2007)},
  year={2007},
  pages={225-230}
}
Distance-based outlier detection is an important data mining technique that finds abnormal data objects according to some distance function. However, when this technique is applied to high-dimensional categorical data, a traditional simple matching dissimilarity measure does not provide an adequate model. In this article, we employ a new common- neighbor-based distance function to measure the proximity between a pair of data points. Experiments show that better outlier mining results can be… CONTINUE READING

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