Satari, Siti Zanariah and Di, Nur Faraidah Muhammad and Zubairi, Yong Zulina and Hussin, Abdul Ghapor (2021) Comparative study of clustering-based outliers detection methods in circular-circular regression model. Sains Malaysiana, 50 (6). pp. 1787-1798. ISSN 0126-6039, DOI https://doi.org/10.17576/jsm-2021-5006-24.
Full text not available from this repository.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 |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | Circular distance; Circular-circular regression model; Clustering; Outliers; Stopping rule |
| Subjects: | Q Science > QA Mathematics |
| Divisions: | Centre for Foundation Studies in Science > Mathematics Division |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 24 Feb 2022 07:01 |
| Last Modified: | 24 Feb 2022 07:01 |
| URI: | http://eprints.um.edu.my/id/eprint/26377 |
Actions (login required)
 |
View Item |
