Detection of Outlier in Uncategorical Dataset using Hybrid Algorithm

Abstract

Outlier mining is an important task of discovering the data records which have an exceptional behavior comparing with other records in the remaining dataset. Outliers do not follow with other data objects in the dataset. There are many effective approaches to detect outliers in numerical data. Most of the earliest work on outlier detection was performed by the statistics community on numeric data. But for categorical dataset there are limited approaches By using NAVF (Normally distributed attribute value frequency) and ROAD (Ranking-based Outlier Analysis and Detection algorithm) and new hybrid approach for outlier detection in categorical dataset will be formed.