Distance-Based Unsupervised Local Outlier Detection: Based Values Analysis to Improve Outlier Detection Using Machine Learning

Machine learning faces challenges in detecting outliers, especially in high-dimensional datasets. Effective data quality is crucial for better results, and many algorithms identify outliers by analysing outlying aspects of data objects and objects within the dataset. The proposed Advanced Distance-Based Unsupervised Local Outlier Detection (DU-LOD) method improves this process by continuously evaluating and identifying outliers using unsupervised learning and distance-based calculations. DU-LOD identifies outliers by comparing differences between data objects and their neighbours, making it the first method to combine unsupervised local outlier detection with nearest cluster point identification. Experimental analysis through accuracy performance of 96.12%, detection rate performance of 41.89%, precision of 56.12%, and recall of 1.79% proves that our model performs best over the various parameters compared with other existing algorithms. Therefore, measures such as area under the ROC curve (AUC), precision and recall are more appropriate in such a scenario.