{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T22:32:15Z","timestamp":1759962735424,"version":"3.41.2"},"reference-count":19,"publisher":"Emerald","issue":"2","license":[{"start":{"date-parts":[[1997,5,1]],"date-time":"1997-05-01T00:00:00Z","timestamp":862444800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[1997,5,1]]},"abstract":"<jats:p>The paper aims to provide a non\u2010technical introduction to the new procedures being used to extract knowledge from databases. The reasons for developing knowledge discovery methods are discussed \u00b1 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\u2010established norms. Examples of the applications of these methods are given. 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