Data Science Asked by Egodym on January 27, 2021
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?
Cluster once.
Study the clusters and refine them to define classes.
Then classify points to these classes.
Answered by Has QUIT--Anony-Mousse on January 27, 2021
You can try
These two have proven better approaches than PCA for time series clustering.
Happy coding ?
Answered by Arpit-Gole on January 27, 2021
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