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FCPS

Fundamental Clustering Problems Suite

The package provides over sixty state-of-the-art clusteringalgorithms for unsupervised machine learning published in.

Table of contents

1. Description
2. Installation
3. Tutorial Examples
4. Manual
5. Use cases
6. Additional information
7. References

Description

The Fundamental Clustering Problems Suite (FCPS) summaries over sixtystate-of-the-art clustering algorithms available in R language. Animportant advantage is that the input and output of clusteringalgorithms is simplified and consistent in order to enable users a swiftexecution of cluster analysis. By combining mirrored-density plots (MDplots) with statistical testing FCPS provides a tool to investigate thecluster tendency quickly prior to the cluster analysis itself.Common clustering challenges can be generated with arbitrary sample size. Additionally, FCPS sums 26indicators with the goal to estimate the number of clusters up andprovides an appropriate implementation of the clustering accuracy formore than two clusters. A subset of methods was used in abenchmarking of algorithms published in.

Installation

Installation using CRAN

Install automatically with all dependencies via

install.packages("FCPS",dependencies = T)# Optionally, for the automatic installation# of all suggested packages:Suggested=c("kernlab","cclust","dbscan","kohonen","MCL","ADPclust","cluster","DatabionicSwarm","orclus","subspace","flexclust","ABCanalysis","apcluster","pracma","EMCluster","pdfCluster","parallelDist","plotly","ProjectionBasedClustering","GeneralizedUmatrix","mstknnclust","densityClust","parallel","energy","R.utils","tclust","Spectrum","genie","protoclust","fastcluster","clusterability","signal","reshape2","PPCI","clustrd","smacof","rgl","prclust","dendextend","moments","prabclus","VarSelLCM","sparcl","mixtools","HDclassif","clustvarsel","knitr","rmarkdown")for(iin1:length(Suggested)) {if (!requireNamespace(Suggested[i],quietly =TRUE)) {message(paste("Installing the package", Suggested[i]))install.packages(Suggested[i],dependencies = T)  }}

Installation using Github

Please note, that dependecies have to be installed manually.

remotes::install_github("Mthrun/FCPS")

Installation using R Studio

Please note, that dependecies have to be installed manually.

Tools -> Install Packages -> Repository (CRAN) ->FCPS

Tutorial Examples

The tutorial with several examples can be found on in the vignette onCRAN:

https://cran.r-project.org/web/packages/FCPS/vignettes/FCPS.html

Manual

The full manual for users or developers is available here:https://cran.r-project.org/web/packages/FCPS/FCPS.pdf

Use Cases

Cluster Analysis ofHigh-dimensional Data

The package FCPS provides a clear and consistent access tostate-of-the-art clustering algorithms:

library(FCPS)data("Leukemia")Data=Leukemia$DistanceClassification=Leukemia$ClsClusterNo=6CA=ADPclustering(Leukemia$DistanceMatrix,ClusterNo)Cls=ClusterRenameDescendingSize(CA$Cls)ClusterPlotMDS(Data,Cls,main = ’Leukemia’,Plotter3D = ’plotly’)ClusterAccuracy(Cls,Classification)[1]0.9963899

set.seed(600)library(FCPS)DataList=ClusterChallenge("Chainlink",SampleSize =750,PlotIt=TRUE)Data=DataList$ChainlinkCls=DataList$Cls>ClusterCount(Cls)$CountPerCluster$NumberOfClusters$ClusterPercentages[1]377373[1]2[1]50.2666749.73333

library(FCPS)set.seed(600)DataList=ClusterChallenge("Chainlink",SampleSize =750)Data=DataList$ChainlinkCls=DataList$Clslibrary(ggplot2)ClusterabilityMDplot(Data)+theme_bw()

library(FCPS)set.seed(135)DataList=ClusterChallenge("Chainlink",SampleSize =900)Data=DataList$ChainlinkCls=DataList$ClsTree=HierarchicalClustering(Data,0,"SingleL")[[3]]ClusterDendrogram(Tree,4,main="Single Linkage")MaximumNumber=7clsm<-matrix(data =0,nrow =dim(Data)[1],ncol = MaximumNumber)for (iin2:(MaximumNumber+1)) {clsm[,i-1]<-cutree(Tree,i)}out=ClusterNoEstimation(Data,ClsMatrix = clsm,MaxClusterNo = MaximumNumber,PlotIt =TRUE)