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I have a dataset of transactional data with customer ID and I want to segment the dataset into groups using cluster analysis. I'm interested in following the evolution of each cluster over time, but since customers have very different behaviours (roughly 50% of the time a customer will change cluster the week after), I was wondering what would be a statistically sound approach. Is it a good idea to train a clustering algorithm every week and look backwards at the weekly evolution of each segment?

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Cluster once.

Study the clusters and refine them to define classes.

Then classify points to these classes.

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