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https://doi.org/10.2498/cit.2006.04.04

Spatio-Temporal Outlier Detection in Large Databases

Alp Kut
Derya Birant

Puni tekst: engleski, pdf (329 KB) str. 291-297 preuzimanja: 1.250* citiraj
APA 6th Edition
Kut, A. i Birant, D. (2006). Spatio-Temporal Outlier Detection in Large Databases. Journal of computing and information technology, 14 (4), 291-297. https://doi.org/10.2498/cit.2006.04.04
MLA 8th Edition
Kut, Alp i Derya Birant. "Spatio-Temporal Outlier Detection in Large Databases." Journal of computing and information technology, vol. 14, br. 4, 2006, str. 291-297. https://doi.org/10.2498/cit.2006.04.04. Citirano 20.02.2020.
Chicago 17th Edition
Kut, Alp i Derya Birant. "Spatio-Temporal Outlier Detection in Large Databases." Journal of computing and information technology 14, br. 4 (2006): 291-297. https://doi.org/10.2498/cit.2006.04.04
Harvard
Kut, A., i Birant, D. (2006). 'Spatio-Temporal Outlier Detection in Large Databases', Journal of computing and information technology, 14(4), str. 291-297. https://doi.org/10.2498/cit.2006.04.04
Vancouver
Kut A, Birant D. Spatio-Temporal Outlier Detection in Large Databases. Journal of computing and information technology [Internet]. 2006 [pristupljeno 20.02.2020.];14(4):291-297. https://doi.org/10.2498/cit.2006.04.04
IEEE
A. Kut i D. Birant, "Spatio-Temporal Outlier Detection in Large Databases", Journal of computing and information technology, vol.14, br. 4, str. 291-297, 2006. [Online]. https://doi.org/10.2498/cit.2006.04.04

Sažetak
Outlier detection is one of the major data mining methods. This paper proposes a three-step approach to detect spatio-temporal outliers in large databases. These steps are clustering, checking spatial neighbors, and checking temporal neighbors. In this paper, we introduce a new outlier detection algorithm to find small groups of data objects that are exceptional when compared with the remaining large amount of data. In contrast to the existing outlier detection algorithms, the new algorithm has the ability of discovering outliers according to the non-spatial, spatial and temporal values of the objects. In order to demonstrate the new algorithm, this paper also presents an example of application using a data warehouse.

Hrčak ID: 44644

URI
https://hrcak.srce.hr/44644

Posjeta: 1.799 *