A modified DBSCAN algorithm for anomaly detection in time-series data with seasonality
Joint Authors
Jain, Praphula
Bajpai, Mani Shankar
Pamula, Rajendra
Source
The International Arab Journal of Information Technology
Issue
Vol. 19, Issue 1 (31 Jan. 2022), pp.23-28, 6 p.
Publisher
Zarqa University Deanship of Scientific Research
Publication Date
2022-01-31
Country of Publication
Jordan
No. of Pages
6
Main Subjects
Information Technology and Computer Science
Abstract EN
Anomaly detection concerns identifying anomalous observations or patterns that are a deviation from the dataset's expected behaviour.
The detection of anomalies has significant and practical applications in several industrial domains such as public health, finance, Information Technology (IT), security, medical, energy, and climate studies.
Density-Based Spatial Clustering of Applications with Noise (DBSCAN) Algorithm is a density-based clustering algorithm with the capability of identifying anomalous data.
In this paper, a modified DBSCAN algorithm is proposed for anomaly detection in time-series data with seasonality.
For experimental evaluation, a monthly temperature dataset was employed and the analysis set forth the advantages of the modified DBSCAN over the standard DBSCAN algorithm for the seasonal datasets.
From the result analysis, we may conclude that DBSCAN is used for finding the anomalies in a dataset but fails to find local anomalies in seasonal data.
The proposed Modified DBSCAN approach helps to find both the global and local anomalies from the seasonal data.
Using normal DBSCAN, we are able to get 19 (2.16%) anomaly points.
While using the modified approach for DBSCAN, we are able to get 42 (4.79%) anomaly points.
In comparison, we can say that we are able to get 2.21% more anomalies using the modified DBSCAN approach.
Hence, the proposed Modified DBSCAN algorithm outperforms in comparison with the DBSCAN algorithm to find local anomalies.
American Psychological Association (APA)
Jain, Praphula& Bajpai, Mani Shankar& Pamula, Rajendra. 2022. A modified DBSCAN algorithm for anomaly detection in time-series data with seasonality. The International Arab Journal of Information Technology،Vol. 19, no. 1, pp.23-28.
https://search.emarefa.net/detail/BIM-1437411
Modern Language Association (MLA)
Jain, Praphula…[et al.]. A modified DBSCAN algorithm for anomaly detection in time-series data with seasonality. The International Arab Journal of Information Technology Vol. 19, no. 1 (Jan. 2022), pp.23-28.
https://search.emarefa.net/detail/BIM-1437411
American Medical Association (AMA)
Jain, Praphula& Bajpai, Mani Shankar& Pamula, Rajendra. A modified DBSCAN algorithm for anomaly detection in time-series data with seasonality. The International Arab Journal of Information Technology. 2022. Vol. 19, no. 1, pp.23-28.
https://search.emarefa.net/detail/BIM-1437411
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 27-28
Record ID
BIM-1437411