I am running k-mean clustering on ~200000 samples. The dataset has in total 14 features. One feature is
id and the rest are categorical.
I have been playing with which features to include in the clustering and the metric Im using is Silhouette.
I would like advice on how to decide which analysis is better. A cluster with fewer features and a higher score (i.e., .8) or a cluster with more features and a lower score (i.e., 30)
My assumption is that the one with more features and a lower score is better because the algorithm has more information that describes the sample. However, those extra features may be making it harder for the algorithm to put the samples into groups.