Data for: kluster: An Efficient Scalable Procedure for Approximating the Number of Clusters in Unsupervised Learning

Published: 19 June 2018| Version 1 | DOI: 10.17632/vfx46vcwpp.1
Hossein Estiri, Behzad Abounia Omrn, Shawn Murphy


182 simulated datasets (first set contains small datasets and second set contains large datasets) with different cluster compositions – i.e., different number clusters and separation values – generated using clusterGeneration package in R. Each set of simulation datasets consists of 91 datasets in comma separated values (csv) format (total of 182 csv files) with 3-15 clusters and 0.1 to 0.7 separation values. Separation values can range between (−0.999, 0.999), where a higher separation value indicates cluster structure with more separable clusters. Size of the dataset, number of clusters, and separation value of the clusters in the dataset is printed in file name. size_X_n_Y_sepval_Z.csv: Size of the dataset = X number of clusters in the dataset = Y separation value of the clusters in the dataset = Z



Data Science, Machine Learning, Cluster Analysis, Clinical Research Informatics