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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References
Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rules Between Sets of Item In Large Databases. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, Washington, DC (1993)
Kotásek, P., Zendulka, J.: Comparison of Three Mining Algorithms For Association Rules. In: Proceeding of MOSIS 2000, Information System Modeling. ISM 2000, Rožnov pod Radhoštěm, Czech Republic, pp. 85–90 (2000)
Han, J., Pei, J., Yin, Y.: Mining Frequent Patterns without Candidate. In: Proc. 2000 ACMSIGMOD Int. Conf. on Management of Data (SIGMOD 2000), Dallas, TX (May 2000)
Lee, G., Lee, K.L., Chen, L.P.: Efficient Graph-Based Algorithms for Discovering and Maintaining Association Rules in Large Databases. In: Knowledge and Information Systems, pp. 338–355. Springer, London (2001)
Srikant, R., Agrawal, R.: Mining Quantitative Association Rule. In: Large Relational Tables. In: Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data (1996)
Fukuda, T., et al.: Mining Optimized Association Rules For Numeric Attributes. In: Proceedings of ACM PODS 1996, Montreal, Canada, pp. 182–191 (1996)
Zhang, W.: Mining Fuzzy Quantitative Association Rules. In: Proceedings of 11th IEEE International Conference on Tools with Artificial Intelligence, Chicago, Illinois, pp. 99–102. IEEE Computer Society, Los Alamitos (1999)
Miller, R.J., Yang, Y.: Association Rules Over Interval Data. In: Proceedings of 1997 ACM SIGMOD, Tucson, Arizona, USA, pp. 452–461 (1997)
Zhang, T., Ramakrishnan, R., Livny, M.: Birch: An Efficient Data Clustering Method For Very Large Databases. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, Montreal, Canada (1996)
Li, J., Shen, H., Topor, R.: An Adaptive Method of Numerical Attribute Merging for Quantitative Association Rule Mining. In: Hui, L.C.K., Lee, D.-L. (eds.) ICSC 1999. LNCS, vol. 1749, pp. 41–50. Springer, Heidelberg (1999)
Hua, Z.: On-Line Analytical Mining of Association Rules, PhD. thesis, Simon Fraser University (1998)
Bartik, V.: Association Rule Discover In Databases. In: Proceedings of 5th International Conference ISM, Roznov pod Radhostem, Czech Republic, pp. 21–27 (2002)
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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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