load("cluster.Rda") pp<-ncol(cluster.fuzzy[[1]]$Clust.desc) k<-ncol(cluster.fuzzy[[1]]$U) cat(paste("Function Objective\t:",cluster.fuzzy[[1]]$func.obj,"\n")) cat(paste("Fuzzyfier\t:",cluster.fuzzy[[1]]$m,"\n")) cat(paste("N Cluster\t:",k,"\n")) cat("Hard Label Partition\t:\n") Label<-cluster.fuzzy[[1]]$Clust.desc[,ncol(cluster.fuzzy[[1]]$Clust.desc)] library(knitr) kable(Label)
Note: this plot can be used to interpret your cluster on data that visualize in 2D via Principal Component Analysis.
Note: Radar Plot can interpret your centroid in a Radar Plot. The value "0" on label means the mean value of variable. The value "+/- 0.5" means the mean value of variable +/- 0.5 standar deviation. And the value "+/- 1" means the mean value of variable +/- standar deviation. The radar plot made this way to be easy understanding and easy on comparison between Cluster Centroid.
Note: This is the exact value of centroid.
library(knitr) Cluster<-colnames(cluster.fuzzy[[1]]$U) colnames(cluster.fuzzy[[1]]$V)->kolomvariabel paste("V",c(1:ncol(cluster.fuzzy[[1]]$V)),sep="")->variabel colnames(cluster.fuzzy[[1]]$V)<-variabel kable(cbind(Cluster,round(cluster.fuzzy[[1]]$V,2))) cat("\nKeterangan Variabel\n") kable(cbind(kolomvariabel, variabel))
Partition can be interpret the value of probability of membership among cluster. The highest partition mean the more probability to grouping to that cluster.
library(knitr) observation<-rownames(cluster.fuzzy[[1]]$Clust.desc) kable(cbind(observation,round(cluster.fuzzy[[1]]$U,2)))
Note: MPC stands from Modified partition coefficient, CE stands from Classification Entropy, XB stands from Xie Beni, and S stands from Separation index.
cat("MPC Index\t:",cluster.fuzzy[[2]][1],"\n") cat("CE Index\t:",cluster.fuzzy[[2]][2],"\n") cat("XB Index\t:",cluster.fuzzy[[2]][3],"\n") cat("S Index\t:",cluster.fuzzy[[2]][4],"\n")
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