Single-linkage method to detect multiple outliers with different outlier scenarios in circular regression model

Single-linkage is one of the algorithms in agglomerative clustering technique that can be used to detect outliers. The single-linkage algorithm combines two clusters with the closest pair of observations. Then, the clusters are combined into larger clusters, until all the observations are formed in the same cluster. In this study, Satari’s single-linkage algorithm method that utilised a circular distance based on the City-block distance as the similarity distance is used to detect multiple outliers for a circular regression model. The aim of this study is to investigate the applicability of the Satari’s single-linkage algorithm in detecting outliers for three different outlier scenarios which are outliers in u-space only, v-space only and both uv-spaces. The performance is measured and tested via simulation studies by calculating the “success” probability (pout), masking error (pmask) and swamping error (pswamp) for all outlier scenarios. It is found that the Satari’s single linkage method performed well in detecting outliers for all three different types of outlier scenarios considered in this study.