Nur Faraidah, Muhammad Di and Siti Zanariah, Satari (2017) The effect of different distance measures in detecting outliers using clustering-based algorithm for circular regression model. AIP Conference Proceedings, 1842 (030016). pp. 1-12. ISSN 978-0-7354-1512-6. (Published)
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Abstract
Outlier detection in linear data sets has been done vigorously but only a small amount of work has been done for outlier detection in circular data. In this study, we proposed multiple outliers detection in circular regression models based on the clustering algorithm. Clustering technique basically utilizes distance measure to define distance between various data points. Here, we introduce the similarity distance based on Euclidean distance for circular model and obtain a cluster tree using the single linkage clustering algorithm. Then, a stopping rule for the cluster tree based on the mean direction and circular standard deviation of the tree height is proposed. We classify the cluster group that exceeds the stopping rule as potential outlier. Our aim is to demonstrate the effectiveness of proposed algorithms with the similarity distances in detecting the outliers. It is found that the proposed methods are performed well and applicable for circular regression model.
| Item Type: | Article |
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| Subjects: | Q Science > QA Mathematics |
| Faculty/Division: | Faculty of Industrial Sciences And Technology |
| Depositing User: | Ms. Siti Zanariah Satari |
| Date Deposited: | 19 Jul 2021 07:45 |
| Last Modified: | 27 Sep 2021 00:46 |
| URI: | http://umpir.ump.edu.my/id/eprint/30297 |
| Download Statistic: | View Download Statistics |
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