  Weighted Outlier Detection is a method for identifying unusual or anomalous
data points in a dataset, which can be caused by various factors like human
error, fraud, or equipment malfunctions. Detecting outliers can reveal vital
information about system faults, fraudulent activities, and patterns in the
data, assisting experts in addressing the root causes of these anomalies.
However,creating a model of normal data patterns to identify outliers can be
challenging due to the nature of input data, labeled data availability, and
specific requirements of the problem. This article proposed the
WePaMaDM-Outlier Detection with distinct mass data mining domain, demonstrating
that such techniques are domain-dependent and usually developed for specific
problem formulations. Nevertheless, similar domains can adapt solutions with
modifications. This work also investigates the significance of data modeling in
outlier detection techniques in surveillance, fault detection, and trend
analysis, also referred to as novelty detection, a semisupervised task where
the algorithm learns to recognize abnormality while being taught the normal
class.
