Domain : Retail I have a set of stores which I want to cluster into similar stores based on 10 variables: revenue, avg income, market share etc. I took 2 approach:
Approach 1: Given there are 10 dimensions its a bit hard to visualize them so I standardised them using standardscaler() and then used tsne to visualize them in 2D. This is how it looks:
The data distribution looks spherical and so I applied kmeans with metric as euclidean on this transformed data.
Approach 2: I did not apply any high dimensional transformation such as pca or tnse and instead just standardised the data and ran clustering algorithms such as HDBSCAN, kmeans, hierarchical etc. The thing is with DBSCAN I am getting all the stores as outliers i.e. cluster -1. With algorithms such as kmeans etc the silhouette cofficient value is pretty less , like close to .15 etc.
I am confused as to how to go about clustering this data set and validating in a proper manner.
Data summary : 2000 data points, 10 variables which are all continuous, although a mixture of absolute number & percentages.