A Novel Approach for Univariate Outlier Detection

Abstract

Abstract-In many applications outlier detection is an important task. In the process of Knowledge Discovery in Databases, isolation of outlying data is important. This isolation process improves the quality of data and reduces the impact of outlying data on the existing values. Numerous methods are available in the detection process of outliers in univariate data sets. Most of these methods handle one outlier at a time. In this paper, Grubb’s statistics, sigma rule and fence rules deal more than one outliers at a time. In general, when multiple outliers are present, presence of such outliers prevents us from detecting other outliers. Hence, as soon as outliers are found, removing outlier is an important task. Multiple outliers are evaluated on different data sets and proved that results are effective. Separate procedures are used for detecting outliers in continuous and discrete data. Experimental results show that our method works well for different data