The Multiple Outliers Detection using Agglomerative Hierarchical Methods in Circular Regression Model

Two agglomerative hierarchical clustering algorithms for identifying multiple outliers in circular regression model have been developed in this study. The agglomerative hierarchical clustering algorithm starts with every single data in a single cluster and it continues to merge with the closest pair...

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Main Authors: Siti Zanariah, Satari, Nur Faraidah, Muhammad Di, Roslinazairimah, Zakaria
Format: Article
Language:English
Published: IOP Publishing 2017
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Online Access:http://umpir.ump.edu.my/id/eprint/18916/1/The%20Multiple%20Outliers%20Detection%20using%20Agglomerative%20Hierarchical%20Methods%20in%20Circular%20Regression%20Model.pdf
http://umpir.ump.edu.my/id/eprint/18916/
http://dx.doi.org/10.1088/1742-6596/890/1/012152
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spelling my.ump.umpir.189162017-11-06T01:50:20Z http://umpir.ump.edu.my/id/eprint/18916/ The Multiple Outliers Detection using Agglomerative Hierarchical Methods in Circular Regression Model Siti Zanariah, Satari Nur Faraidah, Muhammad Di Roslinazairimah, Zakaria Q Science (General) Two agglomerative hierarchical clustering algorithms for identifying multiple outliers in circular regression model have been developed in this study. The agglomerative hierarchical clustering algorithm starts with every single data in a single cluster and it continues to merge with the closest pair of clusters according to some similarity criterion until all the data are grouped in one cluster. The single-linkage method is one of the simplest agglomerative hierarchical methods that is commonly used to detect outlier. In this study, we compared the performance of single-linkage method with another agglomerative hierarchical method, namely average linkage for detecting outlier in circular regression model. The performances of both methods were examined via simulation studies by measuring their "success" probability, masking effect, and swamping effect with different number of sample sizes and level of contaminations. The results show that the single-linkage method performs very well in detecting the multiple outliers with lower masking and swamping effects. IOP Publishing 2017 Article PeerReviewed application/pdf en cc_by http://umpir.ump.edu.my/id/eprint/18916/1/The%20Multiple%20Outliers%20Detection%20using%20Agglomerative%20Hierarchical%20Methods%20in%20Circular%20Regression%20Model.pdf Siti Zanariah, Satari and Nur Faraidah, Muhammad Di and Roslinazairimah, Zakaria (2017) The Multiple Outliers Detection using Agglomerative Hierarchical Methods in Circular Regression Model. Journal of Physics: Conference series, 890 (012152). pp. 1-5. ISSN 1742-6588 (print); 1742-6596 (online) http://dx.doi.org/10.1088/1742-6596/890/1/012152 doi: 10.1088/1742-6596/890/1/012152
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic Q Science (General)
spellingShingle Q Science (General)
Siti Zanariah, Satari
Nur Faraidah, Muhammad Di
Roslinazairimah, Zakaria
The Multiple Outliers Detection using Agglomerative Hierarchical Methods in Circular Regression Model
description Two agglomerative hierarchical clustering algorithms for identifying multiple outliers in circular regression model have been developed in this study. The agglomerative hierarchical clustering algorithm starts with every single data in a single cluster and it continues to merge with the closest pair of clusters according to some similarity criterion until all the data are grouped in one cluster. The single-linkage method is one of the simplest agglomerative hierarchical methods that is commonly used to detect outlier. In this study, we compared the performance of single-linkage method with another agglomerative hierarchical method, namely average linkage for detecting outlier in circular regression model. The performances of both methods were examined via simulation studies by measuring their "success" probability, masking effect, and swamping effect with different number of sample sizes and level of contaminations. The results show that the single-linkage method performs very well in detecting the multiple outliers with lower masking and swamping effects.
format Article
author Siti Zanariah, Satari
Nur Faraidah, Muhammad Di
Roslinazairimah, Zakaria
author_facet Siti Zanariah, Satari
Nur Faraidah, Muhammad Di
Roslinazairimah, Zakaria
author_sort Siti Zanariah, Satari
title The Multiple Outliers Detection using Agglomerative Hierarchical Methods in Circular Regression Model
title_short The Multiple Outliers Detection using Agglomerative Hierarchical Methods in Circular Regression Model
title_full The Multiple Outliers Detection using Agglomerative Hierarchical Methods in Circular Regression Model
title_fullStr The Multiple Outliers Detection using Agglomerative Hierarchical Methods in Circular Regression Model
title_full_unstemmed The Multiple Outliers Detection using Agglomerative Hierarchical Methods in Circular Regression Model
title_sort multiple outliers detection using agglomerative hierarchical methods in circular regression model
publisher IOP Publishing
publishDate 2017
url http://umpir.ump.edu.my/id/eprint/18916/1/The%20Multiple%20Outliers%20Detection%20using%20Agglomerative%20Hierarchical%20Methods%20in%20Circular%20Regression%20Model.pdf
http://umpir.ump.edu.my/id/eprint/18916/
http://dx.doi.org/10.1088/1742-6596/890/1/012152
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score 13.18916