WITHDRAWN: Detecting the outliers in high dimensional datasets using poisson regression

Abstract

Abstract Detecting an Outlier has been a critical issue or problem in the field of machine learning. The issue is about identifying the patterns distinctly different from normal behaviors. Several algorithms have been projected to conquer the challenges as well as explorations in the field of outlier mining, but these methods unable to yields potentially higher accuracy results in such environments. Now a days, developing an efficient method for detecting the outliers in a huge database is a crucial task. In this research article, Poisson Regression technique is projected for outlier’s detections in high dimensional datasets. The proposed methodology is realized in the open source software called Rapid miner. Here, the factors like Average Precision 0.327, Average R-Precision 0.355, MSE 0.154, RMSE 0.2745 and Maximum f_measure 0.452 are calculated using iris dataset. And also the Root relative squared error is 91.48% and relative absolute error 85.09% and Outcomes from experimental analysis illustrate that Poisson method identifies the outliers with potentially higher precision in high dimensional datasets.