A proposed outliers identification algorithm for categorical data sets

Outliers are a minority of observations that are inconsistent with the pattern suggested by the majority of observations. Outliers identification algorithms for categorical data sets face many limitation because measuring distance is not common in categorical data. In this paper, we propose a new unsupervised outliers identification method in categorical data sets. In contrast to other outliers identification methods, the proposed method considers number of categories inside categorical variables. Experimental results show that the proposed method has a comparable performance results with respect to other outliers identification methods in performance.