Say I have a dataset, which consists of
customer is represented by a
orderAmount and a
timestamp (time of day) for when the order was placed. It could look like this:
name |order|orderAmount|timestamp customer1|tv |1 |08:30 customer2|hifi |1 |12:00 customer3|hifi |3 |12:30 customer4|tv |2 |09:30 customer5|cd |10 |10:00 customer1|tv |2 |11:30 ... |... |... |...
What I'm interested in is clustering these customers, so it would be possible for me to clearly differentiate between them. By looking at the above set, it's clear that there's a difference in what, how much and when they buy, but I would like to automate this process, and I assume clustering is one way to do it, but please correct me if I'm wrong.
One thing in particular I'm not sure of is how I would represent this dataset in a "customer-matrix". I know, for example, a distance-based clustering method such as KMeans requires some sort of normalized input, but how would I get started on that with a dataset such as this?
When/if I can produce good clusters, I imagine I could assign labels to these clusters and use these labels to train a classifier?
I'm a beginner to data science, so there might be a step or two missing in my process so please bear with me.