# NOT RUN {
# Reduced K-means for a range of clusters and dimensions
data(macro)
# Cluster quality assessment based on the average silhouette width in the low dimensional space
# nstart = 1 for speed in example
# use more for real applications
bestRKM = tuneclus(macro, 3:4, 2:3, method = "RKM",
criterion = "asw", dst = "low", nstart = 1, seed = 1234)
bestRKM
#plot(bestRKM)
# Cluster Correspondence Analysis for a range of clusters and dimensions
data(bribery)
# Cluster quality assessment based on the Callinski-Harabasz index in the full dimensional space
bestclusCA = tuneclus(bribery, 4:5, 3:4, method = "clusCA",
criterion = "ch", nstart = 20, seed = 1234)
bestclusCA
#plot(bestclusCA, cludesc = TRUE)
# Mixed reduced K-means for a range of clusters and dimensions
data(diamond)
# Cluster quality assessment based on the average silhouette width in the low dimensional space
# nstart = 5 for speed in example
# use more for real applications
bestmixedRKM = tuneclus(diamond[,-7], 3:4, 2:3,
method = "mixedRKM", criterion = "asw", dst = "low",
nstart = 5, seed = 1234)
bestmixedRKM
#plot(bestmixedRKM)
# }
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