RECENT STRATEGIES FOR DETECTING OUTLIERS :A REVIEW

Abstract

Among the study fields, outlier detection is one of the most significant and rapidly evolving. For millennia, outlier defection has been used to identify anomalous occurrences in the data and, if necessary, remove them. One of the most significant and quickly evolving fields of research is outlier detection. Investigations into the critical problem of outlier detection have been conducted in a variety of academic and practical sectors. forth order to provide solutions for properly handling outliers, researchers are putting forth endless effort to develop dependable approaches. In this survey, the researcher offers a thorough analysis and methodical review of how different approaches to outlier detection have evolved over the past 20 years. In this paper, the researcher covers the basic ideas behind outlier detection as well as applications of outlier detection techniques and different approaches to outlier detection. This article offers a deeper understanding of outlier detection approaches and recent advancements in outlier detection techniques, along with an overview of the structure of existing outlier identification tactics. The researcher also categorises the various outliers detecting techniques into different techniques such as distance, clustering, density ensemble, and learning-based methods.