FCPS-package: Fundamental Clustering Problems Suite

In FCPS: Fundamental Clustering Problems Suite

Fundamental Clustering Problems Suite

Description

Over sixty clustering algorithms are provided in this package with consistent input and output, which enables the user to try out algorithms swiftly. Additionally, 26 statistical approaches for the estimation of the number of clusters as well as the mirrored density plot (MD-plot) of clusterability are implemented. The packages is published in Thrun, M.C., Stier Q.: "Fundamental Clustering Algorithms Suite" (2021), SoftwareX, <DOI:10.1016/j.softx.2020.100642>. Moreover, the fundamental clustering problems suite (FCPS) offers a variety of clustering challenges any algorithm should handle when facing real world data, see Thrun, M.C., Ultsch A.: "Clustering Benchmark Datasets Exploiting the Fundamental Clustering Problems" (2020), Data in Brief, <DOI:10.1016/j.dib.2020.105501>.

The package consists of many algorithms and fundamental datasets for clustering published in [Thrun/Stier, 2021]. Originally, the 'Fundamental Clustering Problems Suite' (FCPS) offered a variety of clustering problems any algorithm shall be able to handle when facing real world data. Nine of the here presented artificial datasets were priorly named FCPS with a fixed sample size in Ultsch, A.: "Clustering with SOM: U*C", In Workshop on Self-Organizing Maps, 2005. FCPS often served in the paper as an elementary benchmark for clustering algorithms. The FCPS package extends datasets, enables variable sample sizes for these datasets, and provides a standardized and easy access to many clustering algorithms.

https://www.deepbionics.org/

Details

FCPS datasets consists of data sets with known a priori classification to be reproduced by the algorithms. All data sets are intentionally created to be simple and might be visualized in two or three dimensions. Each data sets represents a certain problem that is solved by known clustering algorithms with varying success. This is done in order to reveal benefits and shortcomings of algorithms in question. Standard clustering methods, e.g. single-linkage, ward and k-means, are not able to solve all FCPS problems satisfactorily. "Lsun3D and each of the nine artificial data sets of "Fundamental Clustering Problems Suite" (FCPS) were defined separately for a specific clustering problem as cited (in [Thrun/Ultsch, 2020]). The original sample size defined in the respective first publication mentioning the data was used in [Thrun/Ultsch, 2020], but using the R function "ClusterChallenge" (...) any sample size can be drawn for all artificial data sets. [Thrun/Ultsch, 2020]

Index: This package was not yet installed at build time.

Author(s)

Michael Thrun [aut, cre, cph] (<https://orcid.org/0000-0001-9542-5543>), Peter Nahrgang [ctr, ctb], Felix Pape [ctr, ctb], Vasyl Pihur [ctb], Guy Brock [ctb], Susmita Datta [ctb], Somnath Datta [ctb], Luis Winckelmann [com], Alfred Ultsch [dtc, ctb], Quirin Stier [ctb, rev]

Maintainer: Michael Thrun <[e-mail]>

References

[Thrun/Ultsch, 2020] Thrun, M. C., & Ultsch, A.: Clustering Benchmark Datasets Exploiting the Fundamental Clustering Problems, Data in Brief, Vol. 30(C), pp. 105501, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.dib.2020.105501")}, 2020.

[Thrun/Stier, 2021] Thrun, M. C., & Stier, Q.: Fundamental Clustering Algorithms Suite SoftwareX, Vol. 13(C), in press, pp. 100642. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.softx.2020.100642")}, 2021.

[Ultsch, 2005] Ultsch, A.: Clustering with SOM: U*C, In Proc. Workshop on Self-Organizing Maps, pp. 75-82, Paris, France, 2005.

FCPS documentation built on Oct. 19, 2023, 5:06 p.m.