K means Clustering

mario_sark

Hello,

I have a quick question, i am build 3 clusters based on RFM Score. R will represent the recent visit to branch , f will represent how often the customer visit within a year , and finally M will represent the amount of money occurs when the customer make a transaction once visit the branch.

once i create the 3 clusters: can re-cluster each cluster into several Clusters based one some variables i choose ?

Thank you
Mario

Telcontar120

Or you might not need just 3 clusters to start with. If you have an RFM schema and each dimension has 5 different values, then you have 125 possible combinations. So k-means doesn't need to start with 3 clusters just because you have 3 dimensions--you could set it to however many clusters you think you want, or run X-Means to see what it would recommend.
But as@yyhuangsaid, if you already have an output target variable in mind, then set it as your label and try a supervised learning algorithm instead. If you want something interpretable, then I have had good results with decision trees and RFM frameworks before.

yyhuang

Hi@mario_sark,

Are you building something like a hierarchical cluster model?

You can try the top-down clustering operator with flatten. But if you have any ground truth tags in the data, better go supervised.




Your output data will have high-level groupinglabeland also low-level detailedcluster ID.

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YY

mario_sark

Hi@yyhuang yyhuang,

Thank you for you reply ,

these my project Steps:
1 -计算RFM
2- Calculate the CP (Customer Power) and give a score
3 - Now i Have as fields : R, F, M, CP
4- Create clusters based on these Variables. (most Prob we want 3 or 4)
5- once we had these clusters we need to do further analysis on each cluster and extract more variables. (maybe 5 variables)
6- now i have more data about my customer in each Cluster. (these that i would use to apply the clustering technique again)

my question was if this is possible to be done. or I have another solution to achieve this Goal

Thank you Again,
Mario