Abstract
The paper describes a new method for association rule discovery in relational databases, which contain both quantitative and categorical attributes. Most of the methods developed in the past are based on initial equi-depth discretization of quantitative attributes. These approaches bring the loss of information. Distance-based methods are another kind of methods. They try to respect the semantics of data. The basic idea of the new method is to separate processing of categorical and quantitative attributes. The first step finds frequent itemsets containing only values of categorical attributes and then quantitative attributes are processed one by one. Discretization of values during quantitative attributes processing is distance-based. A new measure called average distance is introduced for these purposes. The paper describes the method and results of several experiments on real world data.
This work has been supported by the Grant of FRVS MSMT, FR824/2003/G1, “Discovery of Association Rules In Relational Databases” and by the long-term grant project of Ministry of Education No. MSMT 262200012 “Research of information and control systems”.
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Bartík, V., Zendulka, J. (2003). Mining Association Rules from Relational Data – Average Distance Based Method. In: Meersman, R., Tari, Z., Schmidt, D.C. (eds) On The Move to Meaningful Internet Systems 2003: CoopIS, DOA, and ODBASE. OTM 2003. Lecture Notes in Computer Science, vol 2888. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39964-3_48
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DOI: https://doi.org/10.1007/978-3-540-39964-3_48
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