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.
2. What Does Knowledge Discovery in Database (KDD) Mean?
Knowledge discovery in databases (KDD) is the process of discovering useful knowledge from a collection of data. This widely used data mining technique is a process that includes data preparation and selection, data cleansing, incorporating prior
knowledge on data sets and interpreting accurate solutions from the observed results. Major KDD application areas include marketing, fraud detection, telecommunication and manufacturing.