DMCA
Automated Detection of Outliers in Real-World Data (2001)
| Venue: | Proc. of the Second International Conference on Intelligent Technologies |
| Citations: | 7 - 0 self |
BibTeX
@INPROCEEDINGS{Last01automateddetection,
author = {Mark Last and Abraham Kandel},
title = {Automated Detection of Outliers in Real-World Data},
booktitle = {Proc. of the Second International Conference on Intelligent Technologies},
year = {2001},
pages = {292--301}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
Most real-world databases include a certain amount of exceptional values, generally termed as "outliers". The isolation of outliers is important both for improving the quality of original data and for reducing the impact of outlying values in the process of knowledge discovery in databases. Most existing methods of outlier detection are based on manual inspection of graphically represented data. In this paper, we present a new approach to automating the process of detecting and isolating outliers. The process is based on modeling the human perception of exceptional values by using the fuzzy set theory. Separate procedures are developed for detecting outliers in discrete and continuous univariate data. The outlier detection procedures are demonstrated on several standard datasets of varying data quality.
