Applying domain knowledge to knowledge discovery

The amount of data being collected and stored in databases is increasing constantly, because of this the process of knowledge discovery can and will become increasingly time consuming. This thesis researches and shows the advantages of applying previous knowledge about the data, known as domain knowledge, to a data set being analyzed when running knowledge discovery programs. Analysis was completed by running several knowledge discovery hypotheses on sample data and comparing the rules generated when applying domain knowledge and not applying domain knowledge. It was shown that applying domain knowledge clearly helps the knowledge discovery process mostly by reducing the amount of non-interesting rules and amount of time to generate the rules.