Title
Detecting Outliers In Interval Data
Keywords
Data mining; Interval data; Outlier
Abstract
Outlier detection has become an important data mining problem in many applications, including customer management and fraud detection. In recent years, many algorithms have been developed for discovering outliers in large databases. However, to our knowledge, no algorithm exists for discovering outliers in interval data. In this paper, we propose an efficient algorithm to detect distance-based outliers in interval data. We perform empirical studies on real and simulated interval datasets to evaluate the effectiveness of our proposed algorithm in identifying meaningful outliers. Copyright 2006 ACM.
Publication Date
12-1-2006
Publication Title
Proceedings of the Annual Southeast Conference
Volume
2006
Number of Pages
290-295
Document Type
Article; Proceedings Paper
Personal Identifier
scopus
DOI Link
https://doi.org/10.1145/1185448.1185514
Copyright Status
Unknown
Socpus ID
34248355919 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/34248355919
STARS Citation
Li, Shuxin; Lee, Robert; and Lang, Sheau Dong, "Detecting Outliers In Interval Data" (2006). Scopus Export 2000s. 7729.
https://stars.library.ucf.edu/scopus2000/7729