Can we do clustering over several columns in a huge dataframe? I have a dataset stands for customers retail sales data, it includes customer ID, Age, purchases frequency in the 24 month, purchase frequency overall,how many goods cust purchased in the 24 month, how many goods cust purchased overall.
I want to cluster it using Kmeans only based on Age,purchase frequency overall and how many goods cust purchased overall. Then, assigning labels for each customer ID, is that possible?
By the way, I have encounter a memory error if I use Python Agglomerative Clustering. I guess the dataset is quite large, but I don't find any solutions. Any one can give any suggestions?

Yes, clustering with respect to those three features is possible. The problem with Kmeans is that you have to pre-determine the number of clusters and that clusters should have a rather simple form for Kmeans to work properly.
I would strongly suggest first plotting your data, e.g. with some pair-plot library, e.g. in python or in R. That way you get a feeling of whether clustering really makes sense.
There are more powerful clustering algorithms than Kmeans, a very basic and very popular one would be e.g. DBSCAN, which doesn't require a pre-determined cluster count but does require a density parameter.
If you have memory problems, you could either use clustering algorithms that are known to scale well (e.g. BIRCH, but without the final clustering step (parameter n_clusters set to None)) or think of compressing your data first. There are many possibilities.
