Journal of Biomedical and Engineering Research

Techniques for Outlier Detection: A Comprehensive View

Abstract

Richard Murdoch Montgomery

Outlier detection is a critical technique across various domains, including statistics, data science, machine learning, and finance.
Outliers, data points that differ significantly from the majority, can indicate errors, anomalies, or even new insights. This article
provides an in-depth exploration of the primary techniques used to detect outliers, categorized into statistical methods, machine
learning-based approaches, and proximity-based methods. We discuss the advantages, limitations, and specific use cases of
each technique, highlighting their applicability to different types of datasets. The goal is to equip practitioners with a better
understanding of how to identify and handle outliers effectively in real-world data analysis.

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