R: Fundamental Clustering Problems Suite

# Fundamental Clustering Problems Suite

## Documentation for package ‘FCPS’ version 1.4.1

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

## Help Pages

A C D E F G H I K L M N O P Q R S T W

FCPS-package | Fundamental Clustering Problems Suite

## -- A --

AgglomerativeNestingClustering | AGNES clustering
APclustering | Affinity Propagation Clustering
Atom | Atom introduced in [Ultsch, 2004].
AutomaticProjectionBasedClustering | Automatic Projection-Based Clustering

## -- C --

Chainlink | Chainlink introduced in [Ultsch et al., 1994; Ultsch, 1995].
ClusterabilityMDplot | Clusterability MDplot
ClusterAccuracy | ClusterAccuracy
ClusterAlignLabels | Align Cluster Labels to a Reference Clustering
ClusterAMI | Adjusted Mutual Information [Vinh et al., 2009]
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)
ClusterNMI | Normalized Mutual Information [Strehl et al., 2003]
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
ConsensusClustering | Consensus Clustering
CrossEntropyClustering | Cross-Entropy Clustering

## -- D --

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

## -- E --

EngyTime | EngyTime introduced in [Baggenstoss, 2002].
EntropyOfDataField | Entropy Of a Data Field [Wang et al., 2011].
EstimateRadiusByDistance | Estimate Radius By Distance

## -- F --

FannyClustering | Fuzzy Analysis Clustering [Rousseeuw/Kaufman, 1990, p. 253-279]

## -- G --

GapStatistic | Gap Statistic
GenieClustering | Genie Clustering by Gini Index
GolfBall | GolfBall introduced in [Ultsch, 2005]

## -- H --

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 Hierarchical Clustering from Precomputed Dissimilarities
HierarchicalClustering | Unified Wrapper for Hierarchical Clustering
HierarchicalDBSCAN | Hierarchical DBSCAN
Hierarchical_DBSCAN | Hierarchical DBSCAN
Hierarchical_DBscan | Hierarchical DBSCAN

## -- I --

InterClusterDistances | Computes Inter-Cluster Distances
IntraClusterDistances | ClusterDistances
IsDissimilarity | Check whether an object represents a dissimilarity

## -- K --

kmeansClustering | K-Means Clustering
kmeansDist | k-means Clustering using a distance matrix

## -- L --

LargeApplicationClustering | Large Application Clustering
Leukemia | Leukemia distance matrix and classificiation used in [Thrun, 2018]
Lsun3D | Lsun3D inspired by FCPS introduced in [Thrun, 2018]

## -- M --

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].

## -- N --

NetworkClustering | Network Clustering
NeuralGasClustering | Neural gas algorithm for clustering

## -- O --

OPTICSclustering | OPTICS Clustering

## -- P --

PAMClustering | Partitioning Around Medoids (PAM)
PAMclustering | Partitioning Around Medoids (PAM)
parApplyClusterAnalysis | Repeated Application of a Clustering Method
parApplyDataBasedCA | Repeated Application of a Data-Based Clustering Method
parApplyDistanceBasedCA | Repeated Application of a Distance-Based Clustering Method
pdfClustering | Probability Density Distribution Clustering
PFclustering | Power Fuzzy Clustering
ProjectionPursuitClustering | Cluster Identification using Projection Pursuit as described in [Hofmeyr/Pavlidis, 2019].

## -- Q --

QTClustering | Stochastic QT Clustering
QTclustering | Stochastic QT Clustering

## -- R --

RobustTrimmedClustering | Robust Trimmed Clustering

## -- S --

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

## -- T --

TandemClustering | Tandem Clustering
Target | Target introduced in [Ultsch, 2005].
Tetra | Tetra introduced in [Ultsch, 1993]
TwoDiamonds | TwoDiamonds introduced in [Ultsch, 2003a, 2003b]

## -- W --

WingNut | WingNut introduced in [Ultsch, 2005]