Package: FCPS 1.4.1

FCPS: Fundamental Clustering Problems Suite

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>.

Authors:Michael Thrun [aut, cre, cph], 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]

FCPS_1.4.1.tar.gz
FCPS_1.4.1.zip(r-4.7-any)FCPS_1.4.1.zip(r-4.6-any)FCPS_1.4.1.zip(r-4.5-any)
FCPS_1.4.1.tgz(r-4.6-any)FCPS_1.4.1.tgz(r-4.5-any)
FCPS_1.4.1.tar.gz(r-4.7-any)FCPS_1.4.1.tar.gz(r-4.6-any)
FCPS_1.4.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
FCPS/json (API)

# Install 'FCPS' in R:

install.packages('FCPS', repos = c('https://mthrun.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/mthrun/fcps/issues

Datasets:

Atom - Atom introduced in [Ultsch, 2004].
Chainlink - Chainlink introduced in [Ultsch et al., 1994; Ultsch, 1995].
dietary_survey_IBS - Dietary survey IBS [Hayes et al., 2013]
EngyTime - EngyTime introduced in [Baggenstoss, 2002].
GolfBall - GolfBall introduced in [Ultsch, 2005]
Hepta - Hepta introduced in [Ultsch, 2003]
Leukemia - Leukemia distance matrix and classificiation used in [Thrun, 2018]
Lsun3D - Lsun3D inspired by FCPS introduced in [Thrun, 2018]
Target - Target introduced in [Ultsch, 2005].
Tetra - Tetra introduced in [Ultsch, 1993]
TwoDiamonds - TwoDiamonds introduced in [Ultsch, 2003a, 2003b]
WingNut - WingNut introduced in [Ultsch, 2005]

On CRAN:

cluster-analysis cluster-challenge cluster-tendency clustering-algorithms data-mining unsupervised-machine-learning

5.57 score 25 stars 1 packages 744 downloads 74 exports 29 dependencies

Last updated from:7aa98746af. Checks:7 WARNING, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	WARNING	286
source / vignettes	OK	343
linux-release-x86_64	WARNING	293
macos-release-arm64	WARNING	137
macos-oldrel-arm64	WARNING	136
windows-devel-x86_64	WARNING	1331
windows-release-x86_64	WARNING	151
windows-oldrel-x86_64	WARNING	1678
wasm-release	OK	212

Exports:AgglomerativeNestingClustering APclustering AutomaticProjectionBasedClustering ClusterabilityMDplot ClusterAccuracy ClusterAMI ClusterApply ClusterARI ClusterChallenge ClusterCount ClusterCreateClassification ClusterDaviesBouldinIndex ClusterDendrogram ClusterDistances ClusterDunnIndex ClusterEqualWeighting ClusterInterDistances ClusterIntraDistances ClusterMCC ClusterNMI ClusterNoEstimation ClusterNormalize ClusterPlotMDS ClusterRedefine ClusterRename ClusterRenameDescendingSize ClusterShannonInfo ClusterUpsamplingMinority ConsensusClustering CrossEntropyClustering DatabionicSwarmClustering DBSCAN DensityPeakClustering DivisiveAnalysisClustering EntropyOfDataField EstimateRadiusByDistance FannyClustering GapStatistic GenieClustering HCLclustering HDDClustering HierarchicalClustering HierarchicalDBSCAN kmeansClustering LargeApplicationClustering MarkovClustering MeanShiftClustering MinimalEnergyClustering MinimaxLinkageClustering ModelBasedClustering ModelBasedVarSelClustering MoGclustering MSTclustering NetworkClustering NeuralGasClustering OPTICSclustering PAMclustering parApplyClusterAnalysis parApplyDataBasedCA parApplyDistanceBasedCA pdfClustering PFclustering ProjectionPursuitClustering QTclustering RobustTrimmedClustering SharedNearestNeighborClustering SOMclustering SOTAclustering SparseClustering SpectralClustering Spectrum StatPDEdensity SubspaceClustering TandemClustering

Dependencies:cli cpp11 DataVisualizations farver ggplot2 glue gtable isoband labeling lattice lifecycle magrittr mclust plyr pracma R6 RColorBrewer Rcpp RcppParallel reshape2 rlang S7 scales sp stringi stringr vctrs viridisLite withr

The FCPS package

Rendered fromFCPS.Rmdusingknitr::rmarkdown

Last update: 2021-07-09
Started: 2020-06-07

Help page	Topics
Fundamental Clustering Problems Suite	FCPS-package ClusteringAlgorithms
(Adaptive) Density Peak Clustering algorithm using automatic parameter selection	ADPclustering
AGNES clustering	AgglomerativeNestingClustering
Affinity Propagation Clustering	APclustering
Atom introduced in [Ultsch, 2004].	Atom
Automatic Projection-Based Clustering	AutomaticProjectionBasedClustering
Chainlink introduced in [Ultsch et al., 1994; Ultsch, 1995].	Chainlink
Clusterability MDplot	ClusterabilityMDplot
Adjusted Mutual Information [Vinh et al., 2009]	ClusterAMI
Applies a function over grouped data	ClusterApply
Adjusted Rand index	ClusterARI
Generates a Fundamental Clustering Challenge based on specific artificial datasets.	ClusterChallenge
ClusterCount	ClusterCount
Create Classification for Cluster.. functions	ClusterCreateClassification
Davies Bouldin Index	ClusterDaviesBouldinIndex
Cluster Dendrogram	ClusterDendrogram
ClusterDistances	ClusterDistances ClusterIntraDistances IntraClusterDistances
Dunn Index	ClusterDunnIndex
ClusterEqualWeighting	ClusterEqualWeighting
ClusterAccuracy	ClusterAccuracy
Computes Inter-Cluster Distances	ClusterInterDistances InterClusterDistances
Matthews Correlation Coefficient (MCC)	ClusterMCC
Normalized Mutual Information [Strehl et al., 2003]	ClusterNMI
Estimates Number of Clusters using up to 26 Indicators	ClusterNoEstimation
Cluster Normalize	ClusterNormalize
Plot Clustering using Dimensionality Reduction by MDS	ClusterPlotMDS
Redfines Clustering	ClusterRedefine
Renames Clustering	ClusterRename
Cluster Rename Descending Size	ClusterRenameDescendingSize
Shannon Information	ClusterShannonInfo
Cluster Up Sampling using SMOTE for minority cluster	ClusterUpsamplingMinority
Consensus Clustering	ConsensusClustering
Cross-Entropy Clustering	CrossEntropyClustering
DBSCAN	DBSCAN DBscan
Databionic Swarm (DBS) Clustering and Visualization	DatabionicSwarmClustering DBSclusteringAndVisualization
Density Peak Clustering algorithm using the Decision Graph	DensityPeakClustering
Dietary survey IBS [Hayes et al., 2013]	dietary_survey_IBS
Large DivisiveAnalysisClustering Clustering	DivisiveAnalysisClustering
EngyTime introduced in [Baggenstoss, 2002].	EngyTime
Entropy Of a Data Field [Wang et al., 2011].	EntropyOfDataField
Estimate Radius By Distance	EstimateRadiusByDistance
Fuzzy Analysis Clustering [Rousseeuw/Kaufman, 1990, p. 253-279]	FannyClustering
Gap Statistic	GapStatistic
Genie Clustering by Gini Index	GenieClustering
GolfBall introduced in [Ultsch, 2005]	GolfBall
On-line Update (Hard Competitive learning) method	HCLclustering
HDD clustering is a model-based clustering method of [Bouveyron et al., 2007].	HDDClustering
Hepta introduced in [Ultsch, 2003]	Hepta
Internal function of Hierarchical Clusterering of Data	HierarchicalCluster HierarchicalClusterData
Internal Function of Hierarchical Clustering with Distances	HierarchicalClusterDists
Hierarchical Clustering	HierarchicalClustering
Hierarchical DBSCAN	HierarchicalDBSCAN Hierarchical_DBSCAN Hierarchical_DBscan
K-Means Clustering	kmeansClustering
k-means Clustering using a distance matrix	kmeansDist
Large Application Clustering	LargeApplicationClustering
Leukemia distance matrix and classificiation used in [Thrun, 2018]	Leukemia
Lsun3D inspired by FCPS introduced in [Thrun, 2018]	Lsun3D
Markov Clustering	MarkovClustering
Mean Shift Clustering	MeanShiftClustering
Minimal Energy Clustering	MinimalEnergyClustering
Minimax Linkage Hierarchical Clustering	MinimaxLinkageClustering
Model Based Clustering	ModelBasedClustering
Model Based Clustering with Variable Selection	ModelBasedVarSelClustering
Mixture of Gaussians Clustering using EM	MoGclustering
MST-kNN clustering algorithm [Inostroza-Ponta, 2008].	MSTclustering
Network Clustering	NetworkClustering
Neural gas algorithm for clustering	NeuralGasClustering
OPTICS Clustering	OPTICSclustering
Partitioning Around Medoids (PAM)	PAMClustering PAMclustering
Repeated Application of a Clustering Method	parApplyClusterAnalysis
Repeated Application of a Data-Based Clustering Method	parApplyDataBasedCA
Repeated Application of a Distance-Based Clustering Method	parApplyDistanceBasedCA
Probability Density Distribution Clustering	pdfClustering
Penalized Regression-Based Clustering of [Wu et al., 2016].	PenalizedRegressionBasedClustering
Power Fuzzy Clustering	PFclustering
Cluster Identification using Projection Pursuit as described in [Hofmeyr/Pavlidis, 2019].	ProjectionPursuitClustering
Stochastic QT Clustering	QTClustering QTclustering
Random Forest Clustering	RandomForestClustering
Robust Trimmed Clustering	RobustTrimmedClustering
SNN clustering	SharedNearestNeighborClustering
self-organizing maps based clustering implemented by [Wherens, Buydens, 2017].	SOMclustering
SOTA Clustering	SOTAclustering sotaClustering
Sparse Clustering	SparseClustering
Spectral Clustering	SpectralClustering
Fast Adaptive Spectral Clustering [John et al, 2020]	Spectrum
Pareto Density Estimation	StatPDEdensity
Algorithms for Subspace clustering	SubspaceClustering
Tandem Clustering	TandemClustering
Target introduced in [Ultsch, 2005].	Target
Tetra introduced in [Ultsch, 1993]	Tetra
TwoDiamonds introduced in [Ultsch, 2003a, 2003b]	TwoDiamonds
WingNut introduced in [Ultsch, 2005]	WingNut

Package: FCPS 1.4.1

FCPS: Fundamental Clustering Problems Suite

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