I have 1 year transaction level data aggregated at a weekly level for 1000 different stores. I want to cluster similar stores based on 8 variables such as sales, customer count etc. The concern is that as the data is at a store week level I cannot directly run the clustering algorithm as it may compare and classify the data points from same store into one cluster, which is not what is required.
So the second option is to transpose the data i.e. 1 data point for each store having 8(variable) * 52(week) columns and then run clustering on it. I am not sure if k-means or the converntional clustering method will work well on such high dimensional data.
So I am looking for suggestions as to how can we handle this situation.