FCPS1.3.4 package

Fundamental Clustering Problems Suite

ClusterInterDistances

Computes Inter-Cluster Distances

ADPclustering

(Adaptive) Density Peak Clustering algorithm using automatic parameter...

AgglomerativeNestingClustering

AGNES clustering

APclustering

Affinity Propagation Clustering

AutomaticProjectionBasedClustering

Automatic Projection-Based Clustering

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 artific...

ClusterCount

ClusterCount

ClusterCreateClassification

Create Classification for Cluster.. functions

ClusterDaviesBouldinIndex

Davies Bouldin Index

ClusterDendrogram

Cluster Dendrogram

ClusterDistances

ClusterDistances

ClusterDunnIndex

Dunn Index

ClusterEqualWeighting

ClusterEqualWeighting

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

DensityPeakClustering

Density Peak Clustering algorithm using the Decision Graph

DivisiveAnalysisClustering

Large DivisiveAnalysisClustering Clustering

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]

FCPS-package

tools:::Rd_package_title("FCPS")

GapStatistic

Gap Statistic

GenieClustering

Genie Clustering by Gini Index

HCLclustering

On-line Update (Hard Competitive learning) method

HDDClustering

HDD clustering is a model-based clustering method of [Bouveyron et al....

HierarchicalClusterData

Internal function of Hierarchical Clusterering of Data

HierarchicalClusterDists

Internal Function of Hierarchical Clustering with Distances

HierarchicalClustering

Hierarchical Clustering

HierarchicalDBSCAN

Hierarchical DBSCAN

kmeansClustering

K-Means Clustering

kmeansDist

k-means Clustering using a distance matrix

LargeApplicationClustering

Large Application Clustering

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)

pdfClustering

Probability Density Distribution Clustering

PenalizedRegressionBasedClustering

Penalized Regression-Based Clustering of [Wu et al., 2016].

ProjectionPursuitClustering

Cluster Identification using Projection Pursuit as described in [Hofme...

QTclustering

Stochastic QT Clustering

RobustTrimmedClustering

Robust Trimmed Clustering

SharedNearestNeighborClustering

SNN clustering

SOMclustering

self-organizing maps based clustering implemented by [Wherens, Buydens...

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

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>.

  • Maintainer: Michael Thrun
  • License: GPL-3
  • Last published: 2023-10-19