Knowledge Discovery in Data-Mining
Completion requirements
Knowledge discovery in databases (KDD) is discovering useful knowledge from data collection. The data mining process aims to extract information from a data set and transform it into an understandable structure for further use. Data mining is just one step of the knowledge discovery process (the core step). Some following steps are pattern evaluation (this step interprets mined patterns and relationships), akin to your analytic process, and knowledge consolidation, similar to reporting your findings, although they ought to be more robust than simply consolidating knowledge to respond responsibly to your requirements. Like analysis, KDD is an iterative process. If the pattern evaluated after the data mining step is not useful, the process can begin again from the previous steps.
6. Knowledge Discovery Process
MODELS
Although the models usually emphasize independence from specific applications and tools, they can be broadly divided into those that take into account industrial issues and those that do not. However, the academic models, which usually are not concerned with industrial issues, can be made applicable relatively easily in the industrial setting and vice versa. We restrict our discussion to those models that have been popularized in the literature and have been used in real knowledge discovery projects.