Title
Mining Distance-Based Outliers From Categorical Data
Abstract
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 commonneighbor-based distance function to measure the proximity between a pair of data points. Experiments show that better outlier mining results can be achieved when the new distance function is utilized rather than a conventional simple matching dissimilarity measure. © 2007 IEEE.
Publication Date
12-1-2007
Publication Title
Proceedings - IEEE International Conference on Data Mining, ICDM
Number of Pages
225-230
Document Type
Article; Proceedings Paper
Personal Identifier
scopus
DOI Link
https://doi.org/10.1109/ICDMW.2007.75
Copyright Status
Unknown
Socpus ID
49549096188 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/49549096188
STARS Citation
Li, Shuxin; Lee, Robert; and Lang, Sheau Dong, "Mining Distance-Based Outliers From Categorical Data" (2007). Scopus Export 2000s. 6132.
https://stars.library.ucf.edu/scopus2000/6132