Comparative study of clustering-based outliers detection methods in circularcircular 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 method...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Universiti Kebangsaan Malaysia
2021
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| Online Access: | http://umpir.ump.edu.my/id/eprint/31673/1/2021%20Satari%20et%20al%20Sainsmalaysiana.pdf |
| _version_ | 1796994655051579392 |
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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 |
| collection | UMP |
| 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 | 2024-03-06T12:50:54Z |
| format | Article |
| id | UMPir31673 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:50:54Z |
| publishDate | 2021 |
| publisher | Universiti Kebangsaan Malaysia |
| record_format | dspace |
| spelling | UMPir316732021-07-19T07:19:55Z http://umpir.ump.edu.my/id/eprint/31673/ Comparative study of clustering-based outliers detection methods in circularcircular regression model Siti Zanariah, Satari Nur Faraidah, Muhammad Di Yong Zulina, Zubairi Abdul Ghapor, Hussin HA Statistics QA Mathematics 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. Universiti Kebangsaan Malaysia 2021-06-01 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/31673/1/2021%20Satari%20et%20al%20Sainsmalaysiana.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 circularcircular regression model. Sains Malaysiana, 50 (6). pp. 1787-1798. ISSN 0126-6039. (Published) http://doi.org/10.17576/jsm-2021-5006-24 |
| spellingShingle | HA Statistics QA Mathematics Siti Zanariah, Satari Nur Faraidah, Muhammad Di Yong Zulina, Zubairi Abdul Ghapor, Hussin Comparative study of clustering-based outliers detection methods in circularcircular regression model |
| title | Comparative study of clustering-based outliers detection methods in circularcircular regression model |
| title_full | Comparative study of clustering-based outliers detection methods in circularcircular regression model |
| title_fullStr | Comparative study of clustering-based outliers detection methods in circularcircular regression model |
| title_full_unstemmed | Comparative study of clustering-based outliers detection methods in circularcircular regression model |
| title_short | Comparative study of clustering-based outliers detection methods in circularcircular regression model |
| title_sort | comparative study of clustering based outliers detection methods in circularcircular regression model |
| topic | HA Statistics QA Mathematics |
| url | http://umpir.ump.edu.my/id/eprint/31673/1/2021%20Satari%20et%20al%20Sainsmalaysiana.pdf |
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