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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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  • $\begingroup$ Thanks. Any reference to dive deeper? My concern is whether clustering once 2 years of monthly data would yield different results than clustering each month separately and then looking at the results. $\endgroup$ – Egodym May 14 at 22:28
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You can try

  1. Dynamic mode decomposition.
  2. Dynamic Time Warping. Found a nice resource on Towards data science blog.

These two have proven better approaches than PCA for time series clustering.

Happy coding 💻

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