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What main methodology are you using for data mining?


 
  
Poll
What main methodology are you using for data mining? [189 votes total]

CRISP-DM (96) 51%
SEMMA (22) 12%
My organization's (13) 7%
My own (43) 23%
Other (8) 4%
None (7) 4%

Comments


  • Uta Winter, Subject: CRISP-DM
    As the ultimate reason of doing Data Mining is always to improve a business situation which is perceived as being not satisfactory it is only logical that the Crisp-DM methodology starts there: How do we tackle the business problem by transforming it into a DM-problem. It doesn't start with sampling (which is not always necessary anyway)! This is a big improvement over more techically focused approaches. The Crisp-Manual is well-written and especially non-tekkies can benefit from it. After having done some projects myself I can feel that this process model is not written by people who have never been out in a company and only discussing DM in theory but who exacly know all the issues which occur when doing a real project.

  • Markus Gretschmann, Subject: Methodology
    I did several projects according to CRISP and it always worked fine!
    It's the only one that keeps the business goal in mind, not only the technical aspects! It also helps a lot if you have to communicate what you do during a project to someone not involved into the process.

  • Karl Brazier, Subject: Methodology
    Given the choice, I'd be inclined to use my own approach, but in my experience a problem of trying to apply DM in a research group spawned from a more traditional area (in my case, electrical engineering) is that the eminent overseers, lacking a knowledge of DM, try to impose their traditional approach, which turns out to be quite haphazard from the viewpoint of systematic DM. I imagine this will be fairly familiar to others working in groups that are not primarily DM-focussed. Anyway, I've voted for "other", this being a compromise between my own system and no system.

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