Clustering sea waves data in R [closed] - r

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I have proceed in clustering of storm's energy data using different clustering methods (kmeans, hclust, agnes, funny) in R but even if it is easy to choose the best method for my work, I need a computational (and not theoretical) method to compare and evaluate the methods via their results. Do you believe that there is something?
Thanks in advance,

Answer 1

Thanks for the question, I learnt that you could compute optimal number of clusters using eclust function from factoextra package
Using kmeans demo from here
# Load and scale the dataset
data("USArrests")
DF <- scale(USArrests)
When data is not scaledd the clustering results might not be reliable [example](http://stats.stackexchange.com/questions/140711/why-does-gap-statistic-for-k-means-suggest-one-cluster-even-though-there-are-ob)
library("factoextra")
# Enhanced k-means clustering
res.km <- eclust(DF, "kmeans")
# Gap statistic plot
fviz_gap_stat(res.km$gap_stat)
Comparison of Clustering Functions:
You can use all the available methods and compute the optimal number of clusters with:
clusterFuncList = c("kmeans", "pam", "clara", "fanny", "hclust", "agnes" ,"diana")
resultList <- sapply(clusterFuncList,function(x) {
cat("Begin clustering for function:",x,"\n")
#For each clustering function find optimal number of clusters, to disable plotting use graph=FALSE
clustObj = eclust(DF, x,graph=FALSE)
#return optimal number of clusters for each clustering function
cat("End clustering for function:",x,"\n\n\n")
resultDF = data.frame(clustFunc = x, optimalNumbClusters = clustObj$nbclust,stringsAsFactors=FALSE)
})
# >resultList
# clustFunc optimalNumbClusters
# 1 kmeans 4
# 2 pam 4
# 3 clara 5
# 4 fanny 5
# 5 hclust 4
# 6 agnes 4
# 7 diana 4
Gap Statistic i.e. goodness-of-fit measure:
The "gap statistic" is used as a measure of goodness-of-fit for clustering algorithms, see paper
For fixed number of user defined clusters we could compare gap statistic for each clustering algorithm with clusGap function from cluster package:
numbClusters = 5
library(cluster)
clusterFuncFixedK = c("kmeans", "pam", "clara", "fanny")
gapStatList <- do.call(rbind,lapply(clusterFuncFixedK,function(x) {
cat("Begin clustering for function:",x,"\n")
set.seed(42)
#For each clustering function compute gap statistic
gapStatBoot=clusGap(DF,FUNcluster=get(x),K.max=numbClusters)
gapStatVec= round(gapStatBoot$Tab[,"gap"],3)
gapStat_at_AllClusters = paste(gapStatVec,collapse=",")
gapStat_at_chosenCluster = gapStatVec[numbClusters]
#return gap statistic for each clustering function
cat("End clustering for function:",x,"\n\n\n")
resultDF = data.frame(clustFunc = x, gapStat_at_AllClusters = gapStat_at_AllClusters,gapStat_at_chosenCluster = gapStat_at_chosenCluster, stringsAsFactors=FALSE)
}))
# >gapStatList
# clustFunc gapStat_at_AllClusters gapStat_at_chosenCluster
#1 kmeans 0.184,0.235,0.264,0.233,0.27 0.270
#2 pam 0.181,0.253,0.274,0.307,0.303 0.303
#3 clara 0.181,0.253,0.276,0.311,0.315 0.315
#4 fanny 0.181,0.23,0.313,0.351,0.478 0.478
The table above has gap statistic of each algorithm at each clutser from k=1 to 5. Column 3, gapStat_at_chosenCluster has the
gap statistic at k = 5 cluster. The lower the statistic the better the partitioning hence,at k = 5 clusters, kmeans performs better
relative to other algorithms on USArrests dataset