Abstract
Knowledge discovery in database (KDD for short) is fast growing discipline of informatics. It aims at finding new, yet unknown and potentially useful patterns in large databases. Association rules [1] are very popular form of knowledge ex- tracted from databases. The goal of KDD can be e.g. finding interesting patterns concerning dependency of quality of loans on various attributes which can be de- rived from a bank database. These attributes can be arranged e.g. in data matrix LOANS in Tab. 1. There are n loans, each loan corresponds to one row of data matrix. Each column corresponds to an attribute derived from the database. Column TOWN contains information about domicile of the owner of the loan, column AGE contains age of the owner (in years). Column LOAN describes the quality of the loan (OK or BAD). Let us emphasize that usually there are tens of attributes describing both static (TOWN, AGE) and dynamic characteristics (derived e.g. from the transactions) of owners of loans.
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Rauch, J. (1999). Deduction in Logic of Association Rules. In: Thiagarajan, P.S., Yap, R. (eds) Advances in Computing Science — ASIAN’99. ASIAN 1999. Lecture Notes in Computer Science, vol 1742. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46674-6_38
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DOI: https://doi.org/10.1007/3-540-46674-6_38
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