Simulation Study of Agglomerative Clustering Algorithms for Detecting Outliers in Univariate Circular Data
DOI:
https://doi.org/10.22452/mjs.vol45sp2.9Keywords:
Circular data, Agglomerative clustering algorithms, Outlier detection, Outlier scenario, Simulation studyAbstract
Outlier detection techniques for univariate circular data are distinct from those designed for bivariate or multivariate contexts due to differences in data structure and dimensionality. In earlier research, clustering-based procedures specifically those utilizing agglomerative algorithms have proven effective for detecting outliers in bivariate circular datasets. This study aims to evaluate the performance of agglomerative clustering algorithms such as single-linkage (SL), complete-linkage (CL) and average-linkage (AL) by conducting a simulation study for a clustering-based outlier detection (COD) procedure for univariate circular data. The performance of the clustering algorithms from COD procedure assessed across various simulated data conditions under a specific outlier scenario. In the simulation study, outliers are embedded within synthetic datasets using a defined outlier scenario, where the mean direction is shifted by varying degrees of contamination to distinguish outliers from inliers. The simulation study revealed that CL-Satari/Di algorithm has the best performance on detecting outliers correctly with minimal masking effect under specific data conditions. Meanwhile, the performance of SL-Satari/Di algorithm found to be the best algorithm with low swamping effect even though not performing well in detecting correct outliers. This result also revealed that the concentration parameter, percentage of outliers, and sample size affect the performance of the clustering algorithms. The findings demonstrate that the algorithms effectively detect outliers with low masking and swamping effects when the concentration parameter is large. The simulation results also found that the algorithms performed well in detecting outliers when percentage of outliers is small with low masking effect but producing high swamping effect. Moreover, large sample size was found to be less sensitive to masking effect, while a smaller sample size is less sensitive to swamping effect. This work extends existing research on identifying outliers within univariate circular datasets by employing a clustering-based procedure as its core framework.
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