An Interactive Approach to Outlier Detection

Abstract

Abstract. In this paper we describe an interactive approach for finding outliers in big sets of records, such as collected by banks, insurance companies, web shops. The key idea behind our approach is the usage of an easy-to-compute and easy-to-interpret outlier score function. This function is used to identify a set of potential outliers. The outliers, organized in clusters, are then presented to a domain expert, together with some context information, such as characteristics of clusters and distribution of scores. Consequently, they are analyzed, labelled as non-explainable or explainable, and removed from the data. The whole process is iterated several times, until no more interesting outliers can be found.

Large dataset summarization with automatic parameter optimization and parallel processing for local outlier detection

“…Because of the close relationships between data clusters and outliers, clustering analysis can also be performed to assist the detection of outliers by defining outliers as data that do not lie in or located far apart from any clusters. 5,[23][24][25][26][27][28] There has also been some research work on interactive outlier detection, which introduces user-friendly interactive and visualization features to assist outlier detection. 6,[29][30][31][32][33] A comprehensive survey of recent outlier detection techniques has been conducted in other works.…”

“…They usually involve investigating not only the local density of the data being studied but also the local densities of its nearest neighbors. Because of the close relationships between data clusters and outliers, clustering analysis can also be performed to assist the detection of outliers by defining outliers as data that do not lie in or located far apart from any clusters . There has also been some research work on interactive outlier detection, which introduces user‐friendly interactive and visualization features to assist outlier detection .…”