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Maybe you can aggregate the data using fields such as Client ID, merchant ID so that you may also analyze the client and merchant data separately. For example, you can aggregate the data on client id to get the sum or mean of the amount spent by the client. You can further analyze the data by plotting boxplots, distribution plots to find various insights. I ...


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On this data, you can perform a lot of supervised learning. If you know, supervised learning is when the machine learns with data which has labels. In supervised learning, there are two subsets. Those are regression and classification. Classification is when you predict on something which is discrete, such as male or female, or survived or not survived. On ...


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Thanks for your answer. Actually I found out with some extensive research. The key is to define your (individual) meaning of "what you consider to be a cluster" and then derive metrics you want to benchmark those clusters with (could be silhouette coefficient, within cluster sum of squares etc.). Same goes with the assumptions you mentioned. This ...


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My take after a quick read of the references. First of all, Lance and Williams's original paper mentions that their linear scheme works (and offers computational advantage) only for combinatorial strategies. Is minimax linkage such a combinatorial strategy? In other words, does it depend (linearly) on pair-wise distances? By the defintion of minimax distance ...


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