index.KL: Calculates Krzanowski-Lai index

In clusterSim: Searching for Optimal Clustering Procedure for a Data Set

Calculates Krzanowski-Lai index

Description

Calculates Krzanowski-Lai index

Usage

index.KL (x,clall,d=NULL,centrotypes="centroids")

Arguments

x	data
clall	Three vectors of integers indicating the cluster to which each object is allocated in partition of n objects into u-1, u, and u+1 clusters
d	optional distance matrix, used for calculations if centrotypes="medoids"
centrotypes	"centroids" or "medoids"

Details

See file ../doc/indexKL_details.pdf for further details

Value

Krzanowski-Lai index

Author(s)

Marek Walesiak [e-mail], Andrzej Dudek [e-mail]

Department of Econometrics and Computer Science, University of Economics, Wroclaw, Poland

References

Krzanowski, W.J., Lai, Y.T. (1988), A criterion for determining the number of groups in a data set using sum of squares clustering, "Biometrics", 44, 23-34.

Milligan, G.W., Cooper, M.C. (1985), An examination of procedures of determining the number of cluster in a data set, "Psychometrika", vol. 50, no. 2, 159-179. Available at: \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/BF02294245")}.

Tibshirani, R., Walther, G., Hastie, T. (2001), Estimating the number of clusters in a data set via the gap statistic, "Journal of the Royal Statistical Society", ser. B, vol. 63, part 2, 411-423. Available at: \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/1467-9868.00293")}.

See Also

index.G1, index.G2, index.G3, index.C, index.S, index.H, index.Gap, index.DB

Examples

# Example 1
library(clusterSim)
data(data_ratio)
cl1<-pam(data_ratio,4)
cl2<-pam(data_ratio,5)
cl3<-pam(data_ratio,6)
clall<-cbind(cl1$clustering,cl2$clustering,cl3$clustering)
index.KL(data_ratio,clall)

# Example 2
library(clusterSim)
data(data_ratio)
md <- dist(data_ratio, method="manhattan")
# nc - number_of_clusters
min_nc=2
max_nc=15
res <- array(0, c(max_nc-min_nc+1, 2))
res[,1] <- min_nc:max_nc
clusters <- NULL
for (nc in min_nc:max_nc)
{
  if(nc-1==1){
    clustering1<-rep(1,nrow(data_ratio))
  }
  else{
    clustering1 <- pam(md, nc-1, diss=TRUE)$clustering
  }
  clustering2 <- pam(md, nc, diss=TRUE)$clustering
  clustering3 <- pam(md, nc+1, diss=TRUE)$clustering
  clall<- cbind(clustering1, clustering2, clustering3)
  res[nc-min_nc+1,2] <- KL <- index.KL(data_ratio,clall,centrotypes="centroids")
  clusters <- rbind(clusters, clustering2)
} 
print(paste("max KL for",(min_nc:max_nc)[which.max(res[,2])],"clusters=",max(res[,2])))
print("clustering for max KL")
print(clusters[which.max(res[,2]),])
#write.table(res,file="KL_res.csv",sep=";",dec=",",row.names=TRUE,col.names=FALSE)
plot(res,type="p",pch=0,xlab="Number of clusters",ylab="KL",xaxt="n")
axis(1, c(min_nc:max_nc))

# Example 3
library(clusterSim)
data(data_ratio)
md <- dist(data_ratio, method="manhattan")
# nc - number_of_clusters
min_nc=2
max_nc=15
res <- array(0, c(max_nc-min_nc+1, 2))
res[,1] <- min_nc:max_nc
clusters <- NULL
for (nc in min_nc:max_nc)
{
  if(nc-1==1){
    clustering1<-rep(1,nrow(data_ratio))
  }
  else{
    clustering1 <- pam(md, nc-1, diss=TRUE)$clustering
  }
  clustering2 <- pam(md, nc, diss=TRUE)$clustering
  clustering3 <- pam(md, nc+1, diss=TRUE)$clustering
  clall<- cbind(clustering1, clustering2, clustering3)
  res[nc-min_nc+1,2] <- KL <- index.KL(data_ratio,clall,d=md,centrotypes="medoids")
  clusters <- rbind(clusters, clustering2)
} 
print(paste("max KL for",(min_nc:max_nc)[which.max(res[,2])],"clusters=",max(res[,2])))
print("clustering for max KL")
print(clusters[which.max(res[,2]),])
#write.table(res,file="KL_res.csv",sep=";",dec=",",row.names=TRUE,col.names=FALSE)
plot(res,type="p",pch=0,xlab="Number of clusters",ylab="KL",xaxt="n")
axis(1, c(min_nc:max_nc))

clusterSim documentation built on Sept. 30, 2024, 9:15 a.m.