Indian Journal of Science and Technology
Year: 2015, Volume: 8, Issue: 25, Pages: 1-6
Young-Gi Kim and Keon Myung Lee*
Department of Computer Science, Chungbuk National University, Korea; [email protected]
Various methods have been developed to detect outliers which are significantly different from others. Most outlier detection methods assume the data lie in Euclidean space in which distances can be easily defined and computed. In reality, we meet many data with both numerical and categorical attributes together, so-called mixed-data, for which it is not easy to define widely-accepted distance metrics. This paper proposes an outlier detection method which can be applied to mixed data. The method focuses on the association among attribute values. It first selects the sets of potentially associated attributes, computes the degrees of outlierness for records with respect to the associated attributes, and then determines a collection of outliers using the degrees. In addition, this paper shows some experiment results of the proposed method and compares with some other methods.
Keywords: Data Analysis, Data Quality, Horizontal Consistency, Mixed Data, Outlier Detection
Subscribe now for latest articles and news.