As the number and size of very large databases continues to grow rapidly, so does the need to make sense of them. This need is addressed by the field called knowledge Discovery in Databases (KDD), which combines approaches from machine learning, statistics, intelligent databases, and knowledge acquisition. KDD encompasses a number of different discovery methods, such as clustering, data summarization, learning classification rules, finding dependency networks, analysing changes, and detecting anomalies (Matheus et at., 1993).
Matheus C, Chan P and Piatetsky-Shapiro G, 1993. “Systems for knowledge discovery in databases”. IEEE Transactions on Knowledge and Data Engineering. 12.
Abstract: As the number and size of very large databases continues to grow rapidly, so does the need to make sense of them. This need is addressed by the field called knowledge Discovery in Databases (KDD), which combines approaches from machine learning, statistics, intelligent databases, and knowledge acquisition. KDD encompasses a number of different discovery methods, such as clustering, data summarization, learning classification rules, finding dependency networks, analysing changes, and detecting anomalies (Matheus et at., 1993).
