Comparative study of clustering-based outliers detection methods in circular-circular regression model

Document Type

Article

Publication Date

6-1-2021

Abstract

This paper is a comparative study of several algorithms for detecting multiple outliers in circular-circular regression model based on the clustering algorithms. Three measures of similarity based on the circular distance were used to obtain a cluster tree using the agglomerative hierarchical methods. A stopping rule for the cluster tree based on the mean direction and circular standard deviation of the tree height was used as the cutoff point and classifier to the cluster group that exceeded the stopping rule as potential outliers. The performances of the algorithms have been demonstrated using the simulation studies that consider several outlier scenarios with a certain degree of contamination. Application to real data using wind data and a simulated data set are given for illustrative purposes. Thus, it has been found that Satari's algorithm (S-SL algorithm) performs well for any values of sample size n and error concentration parameter. The algorithms are good in identifying outliers which are not limited to one or few outliers only, but the presence of multiple outliers at one time.

Keywords

Circular distance, Circular-circular regression model, Clustering, Outliers, Stopping rule

Publication Title

Sains Malaysiana

Divisions

mathematics

Volume

50

Issue

6

Publisher

Penerbit Universiti Kebangsaan Malaysia

Publisher Location

FACULTY SCIENCE & TECHNOLOGY, BANGI, SELANGOR, 43600, MALAYSIA

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