The R Journal: article published in 2016, volume 8:1

progenyClust: an R package for Progeny Clustering
Chenyue W. Hu and Amina A. Qutub , The R Journal (2016) 8:1, pages 328-338.

Abstract Identifying the optimal number of clusters is a common problem faced by data scientists in various research fields and industry applications. Though many clustering evaluation techniques have been developed to solve this problem, the recently developed algorithm Progeny Clustering is a much faster alternative and one that is relevant to biomedical applications. In this paper, we introduce an R package progenyClust that implements and extends the original Progeny Clustering algorithm for evaluating clustering stability and identifying the optimal cluster number. We illustrate its applicability using two examples: a simulated test dataset for proof-of-concept, and a cell imaging dataset for demonstrating its application potential in biomedical research. The progenyClust package is versatile in that it offers great flexibility for picking methods and tuning parameters. In addition, the default parameter setting as well as the plot and summary methods offered in the package make the application of Progeny Clustering straightforward and coherent.

Received: 2016-01-01; online 2016-05-01
CRAN packages: cclust, clusterSim, cluster, Nbclust, fpc, progenyClust, stat, Hmisc
CRAN Task Views implied by cited CRAN packages: Cluster, Multivariate, Bayesian, ClinicalTrials, Econometrics, Environmetrics, OfficialStatistics, ReproducibleResearch, SocialSciences

This article is licensed under a Creative Commons Attribution 3.0 Unported license .

@article{RJ-2016-023,
  author = {Chenyue W. Hu and Amina A. Qutub},
  title = {{progenyClust: an R package for Progeny Clustering}},
  year = {2016},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2016-023},
  url = {https://doi.org/10.32614/RJ-2016-023},
  pages = {328--338},
  volume = {8},
  number = {1}
}