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008			261006t20262026my a|||fr|||| 000 0 eng d
020			_aTHE0010820(Local) _qHardback
040			_aUMPSA _beng _cUMPSA _erda
090			_aPSM .S93 2026 r Thesis
100	0		_aNur Syahirah Zulkipli, _eauthor.
245	1	0	_aClustering-based procedures for identifying multiple outliers in univariate circular biological data / _cNur Syahirah Zulkipli
264		1	_aKuantan, Pahang : _bUMPSA _c2026
264		4	_c© 2025
300			_axxv, 509 pages : _bIllustrations ; _c30 cm.+ _e1 CD ROM.
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
347			_2rda _atext file _bPDF
500			_aCenter for Mathematical Sciences
502			_aThesis (Doctor of Philosophy) -- Universiti Malaysia Pahang - 2026
504			_aIncludes bibliographical reference
520	3		_aCircular data analysis has gained increasing attention in biological and biomedical research, particularly in improving outlier detection methods for univariate circular data. However, existing methods such as row deletion methods remain limited in detecting multiple outliers in univariate circular data. Although clustering-based procedures have been applied to bivariate circular data, their extension to univariate circular settings remains challenging due to structural differences. Hence, this study proposes a clusteringbased outlier detection (COD) procedure for univariate circular biological data using agglomerative hierarchical clustering algorithms such as single-linkage (SL), completelinkage (CL), and average-linkage (AL), combined with Satari/Di and Chang circular similarity measures to address the limitations of existing methods in detecting multiple outliers. The proposed COD procedure was evaluated through simulation studies based on the von Mises distribution under three outlier scenarios representing different contamination conditions. The findings indicate that the performance of the COD procedure is influenced by the concentration parameter, percentage of outliers and sample size. Overall, the CL-Satari/Di algorithm demonstrated strong outlier detection capability with minimal masking effects, while the SL-Satari/Di algorithm showed greater resistance to swamping effects and provided more stable performance across historical datasets. The proposed COD procedure was further validated using historical univariate circular biological datasets and syndromic craniosynostosis data in Malaysia, where it successfully identified both single and multiple outliers. Moreover, compared with existing A statistic row deletion method, the proposed COD procedure demonstrated improved capability in detecting multiple outliers while reducing masking and swamping effect. This study contributes to circular statistics methodology by extending clusteringbased outlier detection techniques to circular data, providing insight into the behaviour of masking and swamping effects in circular spaces, and also improving the understanding of contamination and cluster separability in univariate circular biological data.
610	2	0	_aCenter for Mathematical Sciences _xDissertations
650		0	_aUniversities and colleges _xDissertations
650		0	_aTheses _xDissertations
942			_2lcc _cTHESIS
999			_c105681 _d105687