Abstract
Knowledge Discovery in Databases (KDD) has become a very attractive discipline both for research and industry within last few years. Its goal is to extract pieces of knowledge or ‘patterns’ from usually very large databases. It portrays a robust sequence of procedures or steps that have to be carried out to derive reasonable and understandable results. One of its components symbolizes an inductive process that induces the above pieces of knowledge; usually it is Machine Learning (ML). However, most of the machine learning algorithms require perfect data in a reasonable format. Therefore, some preprocessing routines as well as postprocessing ones should fill the entire chain of data processing.
This paper overviews and discusses the knowledge discovery process and its methodology as a series of several steps which include machine learning, preprocessing of data, and postprocessing of the results induced.