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 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.