C-Anomalous Assemblage Detection to Recognize Outliers Using kth-Nearest Neighbor Distance

A C anomalous assemblage is a group of associated outliers having the number of instances less than C percent of the total. Presently, known anomaly detection algorithms hardly detect these assemblages without appropriate setting. This paper proposes the new anomalous assemblage detection algorithm called CND which computes a score for an instance using a nearest neighbor distance. The algorithm computes the index k which is equal to the floor function of C percent times the total number of instances and uses the k
^th
-nearest neighbor distance for representing an anomalous score. Moreover, a medcouple from a skew distribution is used to compute the upper threshold for detecting outliers. The algorithm is tested on two collections of datasets which are synthetic datasets and benchmark datasets from Multimedia Analysis and Data Mining website to evaluate the performance comparing with WOF and LOF. The performance of CND is similar to LOF, but it is better than the performance of WOF on the synthetic dataset based on precision” recall and F
₁
-measure.