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One problem with clustering algorithms is that they will typically find you a solution, ie they will split your data set into clusters, but it will find you a structure even if there isn't one. Your data looks like it could consist of about 5 to 7 clusters, but it could equally well just be 2 or only 1. What you need to do after the clustering is to assess ...


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There is this paper Representation and Reinforcement Learning for Personalized Glycemic Control in Septic Patients, presented in the Machine Learning for Health Workshop in NIPS 2017. Here is a quote from the paper where the authors describe the clustering approach: After we generated the state representation, we used the k-means clustering algorithm ...


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