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I have dataset with two label class (good and bad), I want to apply K Means on my dataset using python, should I use that label dataset or I have to delete the label class column ?

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    $\begingroup$ K-means clustering is done to give labels to data. You already have those, so why are you applying k-means? What is the problem statement? $\endgroup$ – bkshi Feb 9 '19 at 8:58
  • $\begingroup$ I think the OP actually meant the dataset contains a binary feature. $\endgroup$ – Louis T Feb 9 '19 at 9:49
  • $\begingroup$ Possible duplicate of K-Means clustering for mixed numeric and categorical data $\endgroup$ – Louis T Feb 9 '19 at 9:50
  • $\begingroup$ Thanks all for your answers , yes my dataset consists of binary features and I want to use clustering to test new samples $\endgroup$ – lona Feb 11 '19 at 3:23
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Delete the label column.

Assuming that you want to compare the clusters to the labels later, then the labels must not be part of the data passed to k-means.

And k-means only works well on continuous variables anyway.

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