I am working on a mall customer segmentation dataset (5 features, 200 rows) using clustering. This dataset does not have any ground truth labels. I had a few doubts regarding clustering:
Can I use model selection in clustering using the silhouette score? - Since my dataset does not have any ground truth labels, I read on the sklearn documentation that you can use Silhouette score to evaluate the performance of the model. Can I use different clustering techniques (like K Means, DBSCAN, Mean shift, etc.) and select the model with the highest silhouette score? The idea is sort of similar to how we do model selection in supervised learning except in the latter we use cross validation.
How do I detect overfitting in clustering? Since the dataset has no labels, I cannot think of a way to identify if the model is overfitting the data.
How do I plot the final clusters when my dataset has more than 2 dimensions? I have seen a lot of visualizations around clustering (like the one below):
Should I use PCA to reduce the features to 2 and then plot the clusters? or is there another way to do this?