Indian Journal of Science and Technology

Abstract

Objective: The objective of this paper is to find the inconsistent objects in data which has high dimension through reduced computation time and increased accuracy. Methods: Hubness specifically Antihubs (points that rarely occur in k nearest neighbor lists) is the newly recognized concept for handling data which has high dimension. The advanced version of Antihub is Antihub2 which is for reconsidering the outlier score of a point obtained by the Antihub method. However, regarding computation time, Antihub2 runs slower. This paper institutes an approach called AdaptiveAntihub2Clust, which is a clustered Adaptive Antihub technique for unsupervised outlier detection to reduce computation time and to improve the accuracy. Findings: The results of an existing Antihub2 method is compared with the proposed method called AdaptiveAntihub2Clust. The experimental results elucidate that AdaptiveAntihub2Clust outperforms well than Antihub2 and also resolved that there is not only a substantial decrease in computation time but also progress in accuracy occurred while the newly built approach is practically used for finding outliers. Applications: The irrelevant objects may ascend due to numerous faults. Detection of such objects identifies the mistakes and fraud before they deteriorate with terrible significances and cleanses the data for further processing.

Keywords: Adaptive Antihub, Antihub, Antihub2, Outliers, Unsupervised