DocumentCode :
3642064
Title :
Anomaly detection in temperature data using DBSCAN algorithm
Author :
Mete Çelik;Filiz Dadaşer-Çelik;Ahmet Şakir Dokuz
Author_Institution :
Dept. of Computer Engineering, Erciyes University, 38039 Kayseri, Turkey
fYear :
2011
fDate :
6/1/2011 12:00:00 AM
Firstpage :
91
Lastpage :
95
Abstract :
Anomaly detection is a problem of finding unexpected patterns in a dataset. Unexpected patterns can be defined as those that do not conform to the general behavior of the dataset. Anomaly detection is important for several application domains such as financial and communication services, public health, and climate studies. In this paper, we focus on discovery of anomalies in monthly temperature data using DBSCAN algorithm. DBSCAN algorithm is a density-based clustering algorithm that has the capability of discovering anomalous data. In the experimental evaluation, we compared the results of DBSCAN algorithm with the results of a statistical method. The analysis showed that DBSCAN has several advantages over the statistical approach on discovering anomalies.
Keywords :
"Clustering algorithms","Temperature distribution","Statistical analysis","Algorithm design and analysis","Time series analysis","Data mining","Meteorology"
Publisher :
ieee
Conference_Titel :
Innovations in Intelligent Systems and Applications (INISTA), 2011 International Symposium on
Print_ISBN :
978-1-61284-919-5
Type :
conf
DOI :
10.1109/INISTA.2011.5946052
Filename :
5946052
Link To Document :
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