I am dealing with a dataset with 13 features.
After going through some standard scaling and missing data imputation, I use kmeans from sklearn to create clusters.
Now the point is that, although the elbow method indicates a 7 cluster model, when I visualise the results I get a colored cloud that to human eye tells nothing. As you can imagine I do not have much experience with clustering so this results tells me personally that the clustering has failed.
What I want to ask here is that apart from the different metrics that may describe the performance of kmeans, shouldn’t the scatter plot of two features be clear in terms of clusters?
Another approach I have taken is that I performed PCA to extract the 2 most important features however I get that I these two only explain ~35-40% of the variance. Nevertheless when I then run the clustering algorithm only on those two features the clusters are extremely well segregated from each other.
Am I missing something ? Apologies if the context here is not clear but any insight are more than welcome.