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I'm looking for a customer clustering solution. I have done a lot of research on the machine learning level to find algorithms that could fit my needs but I can't find much information when the data is mainly text.

I searched for clustering with string but found little relevant ( for me). Do you have any ideas for algorithms that could be used?

Type of relevant data :

  • user right
  • data
  • protocol
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  • $\begingroup$ There's an enormous literature on clustering algorithms, so I think this is too broad to answer in its current form. I suggest you read some standard overview on clustering, examine common clustering algorithms, see whether they meet your needs, and then edit your question to state your requirements, identify which approaches you've already considered, and tell us why you rejected them (what requirements they fail to meet). We expect you to do a significant amount of research before asking here, as there's little point in us repeating standard material that's widely available. $\endgroup$ – D.W. Feb 14 at 9:16
  • $\begingroup$ I may have found a lead that I'm going to explore. Separate the user rights in separate columns and replace the values with 0 or 1 ( or boolean). I should be able to create groups based on rights. I mainly look at algorithms like around kmeans. I also looked for string comparison with Hamming and Levenshtein distance. I've done some research on the subject, but I can't find a lot of information on how to create customer groups, for example based on text data that can be quite diverse. $\endgroup$ – LookingFor Feb 14 at 9:55

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