How to fix kmeans error in r : 'more cluster centers than distinct data points'
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Fix for this is to use :
cells = c(read.csv("c:\\data-files\\kmeans\\cells.csv", header = FALSE))
rnames = c(read.csv("c:\\data-files\\kmeans\\rnames.csv", header = FALSE))
cnames = c(read.csv("c:\\data-files\\kmeans\\cnames.csv", header = FALSE))
instead of
cells = c(read.csv("c:\\data-files\\kmeans\\cells.csv", header = TRUE))
rnames = c(read.csv("c:\\data-files\\kmeans\\rnames.csv", header = TRUE))
cnames = c(read.csv("c:\\data-files\\kmeans\\cnames.csv", header = TRUE))
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blue-sky almost 2 years
When I run a kmeans algorithm I receive this error :
Error in kmeans(x, 2, 15) : more cluster centers than distinct data points.
How can this error be fixed and what does it mean ? I think my data points are distinct ?
Here are my files and the r code I am using to generate kmeans :
rnames.csv : "a1","a2","a3" cells.csv : 0,1,2,1,4,3,5,3,4 cnames.csv : "google","so","test" cells = c(read.csv("c:\\data-files\\kmeans\\cells.csv", header = TRUE)) rnames = c(read.csv("c:\\data-files\\kmeans\\rnames.csv", header = TRUE)) cnames = c(read.csv("c:\\data-files\\kmeans\\cnames.csv", header = TRUE)) x <- matrix(cells, nrow=3, ncol=3, byrow=TRUE, dimnames=list(rnames, cnames)) # run K-Means km <- kmeans(x, 2, 15)