WePaMaDM-Outlier Detection: Weighted Outlier Detection using Pattern Approaches for Mass Data Mining

Avatar
Poster
Voices Powered byElevenlabs logo
Connected to paperThis paper is a preprint and has not been certified by peer review

WePaMaDM-Outlier Detection: Weighted Outlier Detection using Pattern Approaches for Mass Data Mining

Authors

Ravindrakumar Purohit, Jai Prakash Verma, Rachna Jain, Madhuri Bhavsar

Abstract

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.

Follow Us on

2 comments

Avatar
scicastboard

Hello, very interesting paper. We - at ScienceCast - are interested in tools to analyze scientific data (in both STEM and the bio and medical field) with an eye on abnormality detection, red-flagging potentially fraudulent data. Is your research relevant to this problem? Thank you,
ScienceCast Board

Avatar
iamshreeji

Yes

Answer
Add comment