I am working on clustering on binary data which has 25 features,
|sample||Feature 1||Feature 2||Feature 3||......||Feature 25|
and I have used the Silhouette score to choose the number of clusters using the K-modes algorithm, but the score was very low. . I have also tried the HDBSCAN algorithm using Jaccard and hamming distance metric. The silhouette score (around 0.26) was higher than the one using K-modes, but the data distribution was quite unbalanced. Therefore, I would like to ask that are there other better clustering methods for binary data, more appropriate metrics to choose the number of cluster and evaluate the quality of clustering?