Fundamental clustering algorithms suite

The article presents immediate access to over fifty fundamental clustering algorithms. Additionally, access to clustering benchmark datasets published priorly as “Fundamental Clustering Problems Suite” (FCPS) is provided. The software library is named “FCPS”, available in R on CRAN and accessible wi...

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Bibliographic Details
Published in:SoftwareX Vol. 13; p. 100642
Main Authors: Thrun, Michael C., Stier, Quirin
Format: Journal Article
Language:English
Published: Elsevier B.V 01.01.2021
Elsevier
Subjects:
Cluster analysis
Cluster-tendency
Clusterability
Clustering algorithms
Number of clusters
Cluster analysis
Number of clusters
Clustering algorithms
Cluster-tendency
Clusterability
ISSN:2352-7110, 2352-7110
Online Access:Get full text
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Summary:The article presents immediate access to over fifty fundamental clustering algorithms. Additionally, access to clustering benchmark datasets published priorly as “Fundamental Clustering Problems Suite” (FCPS) is provided. The software library is named “FCPS”, available in R on CRAN and accessible within Python. The input and output of clustering algorithms are standardized to enable users a swift execution of cluster analysis. By combining mirrored-density plots (MD plots) with statistical testing, FCPS provides a tool to investigate the cluster-tendency quickly before the cluster analysis itself. Common clustering challenges can be generated with an arbitrary sample size. Additionally, FCPS sums up 26 indicators intending to estimate the number of clusters and provides an appropriate implementation of the clustering accuracy for more than two clusters.
ISSN:2352-7110
2352-7110
DOI:10.1016/j.softx.2020.100642