![]() The solution obtained is not necessarily the same for all starting points. K-means clustering is an iterative method which, wherever it starts from, converges on a solution. Note: if you want to take qualitative variables into account in the clustering, you must first perform a Multiple Correspondence Analysis (MCA) and consider the resulting coordinates of the observations on the factorial axes as new variables. The k-means and AHC methods are therefore complementary. The disadvantage of this method is that it does not give a consistent number of classes or enable the proximity between classes or objects to be determined.
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