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

Siti Zanariah, Satari and Nur Faraidah, Muhammad Di and Yong Zulina, Zubairi and Abdul Ghapor, Hussin (2021) Comparative study of clustering-based outliers detection methods in circularcircular regression model. Sains Malaysiana, 50 (6). pp. 1787-1798. ISSN 0126-6039

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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.

Item Type: Article
Subjects: H Social Sciences > HA Statistics
Q Science > QA Mathematics
Faculty/Division: Center for Mathematical Science
Depositing User: Ms. Siti Zanariah Satari
Date Deposited: 19 Jul 2021 07:19
Last Modified: 19 Jul 2021 07:19
URI: http://umpir.ump.edu.my/id/eprint/31673
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