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Issue No. 06 - December (1993 vol. 5)
ISSN: 1041-4347
pp: 914-925
<p>The authors' perspective of database mining as the confluence of machine learning techniques and the performance emphasis of database technology is presented. Three classes of database mining problems involving classification, associations, and sequences are described. It is argued that these problems can be uniformly viewed as requiring discovery of rules embedded in massive amounts of data. A model and some basic operations for the process of rule discovery are described. It is shown how the database mining problems considered map to this model, and how they can be solved by using the basic operations proposed. An example is given of an algorithm for classification obtained by combining the basic rule discovery operations. This algorithm is efficient in discovering classification rules and has accuracy comparable to ID3, one of the best current classifiers.</p>
database mining; performance perspective; machine learning techniques; classification; associations; sequences; rule discovery; ID3; decision trees; knowledge discovery; DBMS mining; database management systems; decision theory; knowledge based systems; learning (artificial intelligence); performance evaluation

T. Imielinski, R. Agrawal and A. Swami, "Database Mining: A Performance Perspective," in IEEE Transactions on Knowledge & Data Engineering, vol. 5, no. , pp. 914-925, 1993.
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