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
Subjects:	Q Science (General)
Online Access:	http://umpir.ump.edu.my/id/eprint/18916/ http://umpir.ump.edu.my/id/eprint/18916/ http://umpir.ump.edu.my/id/eprint/18916/ http://umpir.ump.edu.my/id/eprint/18916/1/The%20Multiple%20Outliers%20Detection%20using%20Agglomerative%20Hierarchical%20Methods%20in%20Circular%20Regression%20Model.pdf

id	ump-18916
recordtype	eprints
spelling	ump-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
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
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/ http://umpir.ump.edu.my/id/eprint/18916/ http://umpir.ump.edu.my/id/eprint/18916/ http://umpir.ump.edu.my/id/eprint/18916/1/The%20Multiple%20Outliers%20Detection%20using%20Agglomerative%20Hierarchical%20Methods%20in%20Circular%20Regression%20Model.pdf
first_indexed	2023-09-18T22:27:01Z
last_indexed	2023-09-18T22:27:01Z
_version_	1777416034663268352

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

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