Knowledge discovery from databases: an introductory review

The paper aims to provide a non‐technical introduction to the new procedures being used to extract knowledge from databases. The reasons for developing knowledge discovery methods are discussed ± primarily, the current production of very large databases that may include many data relations not explicit in the database structure. The background in machine learning is indicated. The methods used are described for such techniques as classification (sorting data into predefined classes), clustering (developing ab initio a data classification) and the detection of deviations from pre‐established norms. Examples of the applications of these methods are given. The paper concludes with some brief thoughts about the potential use of knowledge discovery in the information field.