Iterative shrinking method for clustering problems

By Pasi Fränti, Olli Virmajoki. Pattern Recognition, 2006

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Balanced K-Means for Clustering (2014, background): “To get better results than in k-means, slower agglomerative algorithms [10,13,12] or more complex k-means variants [3,11,21,18] are sometimes used.”
Robust clustering by detecting density peaks and assigning points based on fuzzy weighted K-nearest neighbors (2016, methodology): “The synthetic datasets used come from research published in [3,10,11,13,14,27] and are commonl used benchmark datasets to test the performance of clustering algorithms.”
dbscan: Fast Density-Based Clustering with R (2019, methodology): “The data sets tested include s1 and s2, the randomly generated but moderately-separated Gaussian clusters often used for agglomerative cluster analysis (Fränti and Virmajoki 2006), the R15 validation data set used for maximum variance based clustering approach by Veenman, Reinders, and Backer (2002)”
Study on density peaks clustering based on k-nearest neighbors and principal component analysis (2016, neutral): “S1, S2, S3 and S4 [31] are four 2-”