Statistical analysis of a hierarchical clustering algorithm with outliers

Klutchnikoff, Nicolas; Poterie, Audrey; Rouvìère, Laurent

doi:10.1016/j.jmva.2022.105075

Cited by 2 publications

(1 citation statement)

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“…The single-linkage algorithm, also known as the minimum method, is one of the most basic agglomerative clustering methods that merges two clusters with the smallest minimum pairwise distance at each iteration. This study focuses on the single-linkage algorithm which is sensitive to the presence of outliers (Klutchnikoff et al, 2021). The singlelinkage algorithm is as follows (Johnson and Wichern, 2014):…”

Section: Clustering-based Procedures For Outlier Detection In Univari...mentioning

confidence: 99%

The Effect of Different Similarity Distance Measures in Detecting Outliers Using Single-Linkage Clustering Algorithm for Univariate Circular Biological Data

Zulkipli

Satari

Yusoff

2022

Pak.j.stat.oper.res.

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The procedure of outliers detection in univariate circular data can be developed using clustering algorithm. In clustering, it is necessary to calculate the similarity measure in order to cluster the observations into their own group. The similarity measure in circular data can be determined by calculating circular distance between each point of angular observation. In this paper, clustering-based procedure for outlier detection in univariate circular biological data with different similarity distance measures will be developed and the performance will be investigated. Three different circular similarity distance measures are used for the outliers detection procedure using single-linkage clustering algorithm. However, there are two similarity measures namely Satari distance and Di distance that are found to have similarity in formula for univariate circular data. The aim of this study is to develop and demonstrate the effectiveness of proposed clustering-based procedure with different similarity distance measure in detecting outliers. Therefore, in this study the circular similarity distance of SL-Satari/Di and another similarity measure namely SL-Chang will be compared at certain cutting rule. It is found that clustering-based procedure using single-linkage algorithm with different similarity distances are applicable and promising approach for outlier detection in univariate circular data, particularly for biological data. The result also found that at a certain condition of data, the SL-Satari/Di distance seems to overperform the performance of SL-Chang distance.

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Section: Clustering-based Procedures For Outlier Detection In Univari...mentioning

confidence: 99%