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Not reproducible results with find.clusters #335
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Responding my findings here because I myself was looking for an answer to a similar problem. Hopefully this is useful for other users. I've found this in another thread:
Original reference: Otherwise, it would be worth increasing the number of runs of k-means ( EDIT: just as an example, for my data the analysis stabilised for |
I try using the find.clusters function with the phenotypic data of wheat (you can think of my data set similar to USArrets dataset) for the purpose of cutting the dendrogram into these number of clusters. But every time the sequence of cluster changes like if first cluster having 4 members, second as 2 members etc. then repeating the function with similar conditions give first cluster with, say, 5 members and so on. Not reproducible results.
#df is my dataset
foo.BIC <- find.clusters(df, max.n = 20, n.pca =200, scale = FALSE,
stat = "BIC", method = "kmeans")
plot(foo.BIC$Kstat, type="o", xlab="number of clusters (K)", ylab="BIC",
col="green", main="Detection based on BIC")
points(5, foo.BIC$Kstat[5], pch="x", cex=3)
mtext(3, tex="'X' indicates the actual number of clusters")
foo.BIC$size
foo.BIC$grp
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