MARC details
| 000 -LEADER |
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03424ntm a2200337 i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
MY-KuUP |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20261006155100.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
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t||||fr|||| 000 0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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ta |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
261006t20262026my a|||fr|||| 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
THE0010820(Local) |
| Qualifying information |
Hardback |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
UMPSA |
| Language of cataloging |
eng |
| Transcribing agency |
UMPSA |
| Description conventions |
rda |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
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| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
PSM .S93 2026 r Thesis |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Nur Syahirah Zulkipli, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
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| Title |
Clustering-based procedures for identifying multiple outliers in univariate circular biological data / |
| Statement of responsibility, etc. |
Nur Syahirah Zulkipli |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
Kuantan, Pahang : |
| Name of producer, publisher, distributor, manufacturer |
UMPSA |
| Date of production, publication, distribution, manufacture, or copyright notice |
2026 |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Date of production, publication, distribution, manufacture, or copyright notice |
© 2025 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
xxv, 509 pages : |
| Other physical details |
Illustrations ; |
| Dimensions |
30 cm.+ |
| Accompanying material |
1 CD ROM. |
| 336 ## - CONTENT TYPE |
|---|
| Source |
rdacontent |
| Content type term |
text |
| 337 ## - MEDIA TYPE |
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| Source |
rdamedia |
| Media type term |
unmediated |
| 338 ## - CARRIER TYPE |
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| Source |
rdacarrier |
| Carrier type term |
volume |
| 347 ## - DIGITAL FILE CHARACTERISTICS |
|---|
| Source |
rda |
| File type |
text file |
| Encoding format |
PDF |
| 500 ## - GENERAL NOTE |
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| General note |
Center for Mathematical Sciences |
| 502 ## - DISSERTATION NOTE |
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| Dissertation note |
Thesis (Doctor of Philosophy) -- Universiti Malaysia Pahang - 2026 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc. note |
Includes bibliographical reference |
| 520 3# - SUMMARY, ETC. |
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| Summary, etc. |
Circular 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 20 - SUBJECT ADDED ENTRY--CORPORATE NAME |
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| Corporate name or jurisdiction name as entry element |
Center for Mathematical Sciences |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Universities and colleges |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Theses |
| General subdivision |
Dissertations |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Thesis |