Anomaly detection in temperature data using DBSCAN algorithm

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 heal...

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Bibliographic Details
Main Authors: Celik, M., Dadaser-Celik, F., Dokuz, A. S.
Format: Conference Proceeding
Language:English
Subjects:
Algorithm design and analysis
anamoly detection in time series data
Clustering algorithms
Data mining
DBSCAN algorithm
Meteorology
Statistical analysis
temperature data
Temperature distribution
Time series analysis
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Summary: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.
DOI:10.1109/INISTA.2011.5946052