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I'm using K-means for unsupervised learning, using data vectors with an IP address and a language. I need to represent them in an abstract way, so that I can use this algorithm.

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  • $\begingroup$ What is an Ip? Do you mean IP Address? $\endgroup$ – Wandering Logic Aug 26 '15 at 12:26
  • $\begingroup$ Yes, an IP address. I should have explained it better. $\endgroup$ – Nuno Mendes Aug 26 '15 at 13:21
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If your data doesn't map in an obvious way onto vectors of continuous real numbers, don't use K-means.

To use K-means clustering you need to have a well defined distance between any two points in the space. To do that you need to have a notion of distance for each axis.

Typically discrete classifications like "language" don't have any natural mapping onto real numbers.

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  • $\begingroup$ So, is there a way to make classification on problems of this genre? I have tought of PCA, but It's the same problem. $\endgroup$ – Nuno Mendes Aug 26 '15 at 13:26

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