Fundamental Clustering Problems Suite

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Documentation for package ‘FCPS’ version 1.3.4

• DESCRIPTION file.
• User guides, package vignettes and other documentation.

Help Pages

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