### Clustering: Hierarchical and K-Means in R on Hofstede Cultural Patterns

Overview
What follows is an exploration of clustering, via hierarchies and K-Means, using the Hofstede patterns data, available from my Public folder.

For a deeper understanding of clustering and the various related techniques I suggest the following: Cluster analysis (Wikipedia)An Introduction to Clustering and different methods of clustering Load Data # load data Hofstede.df.preclean <- read.csv("HofstedePatterns.csv", na.strings = c("", "NA")) #nrow(Hofstede.df.preclean) # remove NULLs Hofstede.df.preclean <- na.omit(Hofstede.df.preclean) #nrow(Hofstede.df.preclean) Hofstede.df <- Hofstede.df.preclean Hierarchical Clustering Run hclust, Generate Dendrogram The first attempt is the simplest analysis using the dist() and hclust() functions to generate a hierarchy of grouped data. The cluster size is derived from a reading of the dendrogram, although there are automated ways of selecting the cluster number, shown …

For a deeper understanding of clustering and the various related techniques I suggest the following: Cluster analysis (Wikipedia)An Introduction to Clustering and different methods of clustering Load Data # load data Hofstede.df.preclean <- read.csv("HofstedePatterns.csv", na.strings = c("", "NA")) #nrow(Hofstede.df.preclean) # remove NULLs Hofstede.df.preclean <- na.omit(Hofstede.df.preclean) #nrow(Hofstede.df.preclean) Hofstede.df <- Hofstede.df.preclean Hierarchical Clustering Run hclust, Generate Dendrogram The first attempt is the simplest analysis using the dist() and hclust() functions to generate a hierarchy of grouped data. The cluster size is derived from a reading of the dendrogram, although there are automated ways of selecting the cluster number, shown …