Scalable Unsupervised Algorithms for Anomaly Detection in Field Monitoring Systems

• Author(s): Newness Skymax
• Paper ID: 1704393
• Page: 906-909
• Published Date: 31-05-2023
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 6 Issue 11 May-2023

Abstract

Anomaly detection is a crucial component of field monitoring systems, especially in scenarios where systems need to operate in real-time, continuously gathering data across vast environments. The complexity and scale of data in such systems demand efficient and scalable algorithms to detect irregularities or anomalies. Traditional anomaly detection methods often rely on supervised learning models, which require labeled data for training, posing challenges in real-world applications where labels are sparse or unavailable. Unsupervised learning techniques, however, do not require labeled data and have gained prominence for their ability to handle large-scale, complex datasets with minimal human intervention. This article explores the use of scalable unsupervised anomaly detection algorithms in field monitoring systems, discussing their advantages, challenges, and the state-of-the-art techniques employed in these systems. We analyze key algorithms such as clustering-based methods, distance-based methods, and neural network-based approaches, evaluating their applicability, scalability, and effectiveness in real-world applications. By examining recent advancements, this article highlights the future potential and emerging trends in unsupervised anomaly detection for field monitoring.

Keywords

Anomaly detection, unsupervised learning, field monitoring systems, scalable algorithms, clustering, distance-based methods, neural networks, real-time data, data analytics, machine learning.

Citations

IRE Journals:
Newness Skymax "Scalable Unsupervised Algorithms for Anomaly Detection in Field Monitoring Systems" Iconic Research And Engineering Journals Volume 6 Issue 11 2023 Page 906-909

IEEE:
Newness Skymax "Scalable Unsupervised Algorithms for Anomaly Detection in Field Monitoring Systems" Iconic Research And Engineering Journals, 6(11)