I am using k-modes for clustering unlabeled categorical data. Since the data is not labeled, I fear that my knowledge of suitable valuation indices is limited. So far I have used silhouette score with a precomputed matrix of Ng's dissimilarity measure as presented in: Ng, M. K., Li, M. J., Huang, J. Z., & He, Z. (2007). On the impact of dissimilarity measure in k-modes clustering algorithm. IEEE transactions on pattern analysis and machine intelligence, 29(3), 503-507.
- My first question is whether it is valid to use the silhouette score with a self-defined dissimilarity measure. So far I just assumed that it is, because the python implementation sklearn.metrics.silhouette_score allows it.
- My second question is whether I can also use indices like Calinski-Harabasz, or Davies-Bouldin, as these are actually based on distances in Euclidean space, but I use it for categorical data clusters. My assumption so far is that it can still be used as a quality measure for the results of the k-modes algorithm.
- My last question is whether you have any better suggestions for a suitable method for evaluating the cluster results of categorical data.