Knowledge Discovery and Data Mining Process Model
I read an article (Kurgan et al. 2006) reviewing several commonly used process models for knowledge discovery and data mining recently. The number of steps in these models ranged from 5 to 9 but the actual process is pretty similar among the models. Well, I guess you can’t deviate too much if you wish to do knowledge discovery properly.
Among the various models presented, I particularly like the Generic model, which pools and summarizes the important points from the reviewed models. The Generic model borrows heavily on a model proposed by Cios et al. in 2000. The steps in the Generic model are:
- Application domain understanding
- Data understanding
- Data preparation and identification of data mining technology
- Data mining
- Evaluation
- Knowledge consolidation and deployment
I am sure these steps are nothing new and will be familiar to those involved in knowledge discovery and data mining. However, if you had not been following any particular models, this might serve as a good reference to show that the methods which you had been using were already validated by others.
References
- Cios KJ, Teresinska A, Konieczna S, Potocka J, Sharma S. A knowledge discovery approach to diagnosing myocardial perfusion. Engineering in Medicine and Biology Magazine, IEEE. 2000;19(4):17-25.
- Kurgan LA, Musilek P. A survey of Knowledge Discovery and Data Mining process models. The Knowledge Engineering Review. 2006;21(01):1-24.