Comparative study of clustering-based outliers detection methods in circular-circular regression model
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 meth...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Penerbit Universiti Kebangsaan Malaysia
2021
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| Online Access: | http://journalarticle.ukm.my/17542/ http://journalarticle.ukm.my/17542/1/24.pdf |
| _version_ | 1848814339540123648 |
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| author | Siti Zanariah Satari, Nur Faraidah Muhammad Di, Yong Zulina Zubairi, Abdul Ghapor Hussin, |
| author_facet | Siti Zanariah Satari, Nur Faraidah Muhammad Di, Yong Zulina Zubairi, Abdul Ghapor Hussin, |
| author_sort | Siti Zanariah Satari, |
| building | UKM Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T00:32:31Z |
| format | Article |
| id | oai:generic.eprints.org:17542 |
| institution | Universiti Kebangasaan Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T00:32:31Z |
| publishDate | 2021 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:generic.eprints.org:175422021-10-26T06:27:07Z http://journalarticle.ukm.my/17542/ Comparative study of clustering-based outliers detection methods in circular-circular regression model Siti Zanariah Satari, Nur Faraidah Muhammad Di, Yong Zulina Zubairi, Abdul Ghapor Hussin, 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. Penerbit Universiti Kebangsaan Malaysia 2021-06 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/17542/1/24.pdf 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 circular-circular regression model. Sains Malaysiana, 50 (6). pp. 1787-1798. ISSN 0126-6039 https://www.ukm.my/jsm/malay_journals/jilid50bil6_2021/KandunganJilid50Bil6_2021.html |
| spellingShingle | Siti Zanariah Satari, Nur Faraidah Muhammad Di, Yong Zulina Zubairi, Abdul Ghapor Hussin, Comparative study of clustering-based outliers detection methods in circular-circular regression model |
| title | Comparative study of clustering-based outliers detection methods in circular-circular regression model |
| title_full | Comparative study of clustering-based outliers detection methods in circular-circular regression model |
| title_fullStr | Comparative study of clustering-based outliers detection methods in circular-circular regression model |
| title_full_unstemmed | Comparative study of clustering-based outliers detection methods in circular-circular regression model |
| title_short | Comparative study of clustering-based outliers detection methods in circular-circular regression model |
| title_sort | comparative study of clustering-based outliers detection methods in circular-circular regression model |
| url | http://journalarticle.ukm.my/17542/ http://journalarticle.ukm.my/17542/ http://journalarticle.ukm.my/17542/1/24.pdf |