Closest fit Approach for Atypical Value Revealing and Deciles Range Anomaly Detection Method for Recovering Misplaced value in Data Mining

Abstract

In identifying anomalous database values, it is currently a very active research area in the mining community. The task of identifying anomalous values is to find a small group of exceptional data objects compared to the rest of the large amount of data. The discovery of anomalous values in a group of models is an extremely recognized difficulty in the field of data mining. An outlier is a prototype that is not related to the rest of the patterns in the data set. The proposed method for searching for outliers uses an anomalous detection approach. The purpose of the approach is to find anomalous values first based on the criteria of the condition.We use the information criterion and approach named Outlier Detection to remove the outliers from the dataset and apply deciles range anomaly detection algorithm for Recovery algorithm to recover missing data from the database.