I understand how to use Euclidean distance to assign instances to clusters for numerical attributes, and how to use the number of correct binary values to assign instances to clusters for nominal attributes, but how does simple k-means assign instances to clusters when instances have both numeric and nominal attributes?   The number of “hits” on the nominal attribute is a measure of closeness to a nominal cluster center, except that the larger this value the closer an instance is to the cluster center, but how would this measure be combined with a Euclidean distance for numerical attributes to find the closest cluster?


Thanks!
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Mark Polczynski