Fundamental Clustering Problems Suite
Computes Inter-Cluster Distances
(Adaptive) Density Peak Clustering algorithm using automatic parameter...
AGNES clustering
Affinity Propagation Clustering
Automatic Projection-Based Clustering
Clusterability MDplot
ClusterAccuracy
Applies a function over grouped data
Adjusted Rand index
Generates a Fundamental Clustering Challenge based on specific artific...
ClusterCount
Create Classification for Cluster.. functions
Davies Bouldin Index
Cluster Dendrogram
ClusterDistances
Dunn Index
ClusterEqualWeighting
Matthews Correlation Coefficient (MCC)
Estimates Number of Clusters using up to 26 Indicators
Cluster Normalize
Plot Clustering using Dimensionality Reduction by MDS
Redfines Clustering
Renames Clustering
Cluster Rename Descending Size
Shannon Information
Cluster Up Sampling using SMOTE for minority cluster
Cross-Entropy Clustering
Databionic Swarm (DBS) Clustering and Visualization
DBSCAN
Density Peak Clustering algorithm using the Decision Graph
Large DivisiveAnalysisClustering Clustering
Entropy Of a Data Field [Wang et al., 2011].
Estimate Radius By Distance
Fuzzy Analysis Clustering [Rousseeuw/Kaufman, 1990, p. 253-279]
tools:::Rd_package_title("FCPS")
Gap Statistic
Genie Clustering by Gini Index
On-line Update (Hard Competitive learning) method
HDD clustering is a model-based clustering method of [Bouveyron et al....
Internal function of Hierarchical Clusterering of Data
Internal Function of Hierarchical Clustering with Distances
Hierarchical Clustering
Hierarchical DBSCAN
K-Means Clustering
k-means Clustering using a distance matrix
Large Application Clustering
Markov Clustering
Mean Shift Clustering
Minimal Energy Clustering
Minimax Linkage Hierarchical Clustering
Model Based Clustering
Model Based Clustering with Variable Selection
Mixture of Gaussians Clustering using EM
MST-kNN clustering algorithm [Inostroza-Ponta, 2008].
Network Clustering
Neural gas algorithm for clustering
OPTICS Clustering
Partitioning Around Medoids (PAM)
Probability Density Distribution Clustering
Penalized Regression-Based Clustering of [Wu et al., 2016].
Cluster Identification using Projection Pursuit as described in [Hofme...
Stochastic QT Clustering
Robust Trimmed Clustering
SNN clustering
self-organizing maps based clustering implemented by [Wherens, Buydens...
SOTA Clustering
Sparse Clustering
Spectral Clustering
Fast Adaptive Spectral Clustering [John et al, 2020]
Pareto Density Estimation
Algorithms for Subspace clustering
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>.