Methods of Assigning Labels to Detect Outliers

Abstract

Outlier identification is a crucial field within data mining that focuses on identifying data points that significantly depart from other patterns in the data. Outlier identification may be categorized into formal and informal procedures. This article discusses informal approaches, sometimes known as labelling methods. The study focused on the analysis of real-time medical data to identify outliers using outlier labelling techniques. Various labelling approaches are used to calculate realistic situations in the dataset. Ultimately, using the anticipated outcomes of the outliers is a more suitable approach for addressing the needs of the larger populations.