An extended process model of knowledge discovery in database

Much research on knowledge discovery in database (KDD) merely pays attention to data mining, one of many interacting steps in the process of discovering previously unknown and potentially interesting patterns in large databases, but little to the whole process. However, such approaches cannot satisfy the need of real applications of KDD. The purpose of this work is to extend a process model of KDD in practice at large.

A new model based on research experiences of the knowledge discovery process is formalized as an extension of the model by Fayyad et al. A case study by a reduct method from rough set theory is to illustrate why the process model is proposed and in what situation it can be used in practice.

This model incorporates data collection in the KDD process to supply a sound framework to better support KDD applications.

This model reflects the native of KDD in some tested cases. It may need further research to be used in all other situations.

It can be used in the area of information security, medical treatment and other information management.

Using this model, one can directly collect data that are essential and useful for the mining results. It also offers practical help to those KDD researchers both from industry and academia.