A Maximum Variance Cluster Algorithm

By Cor J. Veenman, Marcel J. T. Reinders, Eric Backer. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002

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Study on density peaks clustering based on k-nearest neighbors and principal component analysis (2016, methodology): “The ﬁrst data set, R15 [28] is generated as 15 similar 2-”
DBSVEC: Density-Based Clustering Using Support Vector Expansion (2019, methodology): “Next, we examine the accuracy of DBSVEC on other open datasets from different domains including Seeds [33], Dim32 and Dim64 [34], Map-Joensuu and MapFinland5, D31 [35], Breast-Cancer [33], Miss-America [36], House [36], t4.”
dbscan: Fast Density-Based Clustering with R (2019, methodology): “…randomly generated but moderately-separated Gaussian clusters often used for agglomerative cluster analysis (Fr ¨ anti and Virmajoki 2006), the R15 validation data set used for maximum variance based clustering approach by Veenman, Reinders, and Backer (2002), the well-known spatial data set t4.”