How to handle categorical features in K-means? I am working on clustering algorithms. I am working with titanic dataset. It contains 6 categorical features. I used k-means algorithm on this dataset. I am using label encoding for categorical features. But I found that categorical features should use euclidean distance. It should use Hamming distance. So, how to make k-means work finely on mixed features? I don't need other algorithm. I just want to work with k-means only on mixed features dataset.
Label encoding is not a good idea if the nature of categories are not ordinal (it is actually not my favorite anyways). Use one-hot encoding and see how it works. You may apply a feature extraction on top of it, e.g. PCA, to reduce the noise coming from sparsity. The other idea is to label categories by their fraction in the feature, for example:
[a,b,b,c,a,a] --&gt; [3/6, 2/6, 2/6, 1/6, 3/6, 3/6]