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Association Rules in Incomplete Databases

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Book cover Methodologies for Knowledge Discovery and Data Mining (PAKDD 1999)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1574))

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Abstract

Discovering association rules among items in large databases in recognized as an important database mining problem. The problem has been introduced originally for sales transaction database and did not relate to missing data. However, missing data often occur in relational databases, especially in business ones. It is not obvious how to compute association rules from such incomplete databases. It is provided and proved in the paper how to estimate support and confidence of an association rule induced from an incomplete relational database. We also introduce definitions of expected support and confidence of an association rule. The proposed definitions guarantee some required properties of itemsets and association rules. Eventually, we discuss another approach to missing values based on so called valid databases and compare both approaches.

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References

  1. Agraval, R., Imielinski, T., Swami, A.: Mining Associations Rules between Sets of Items in Large Databases. In: Proc. of the ACM SIGMOD Conference on Management of Data. Washington, D.C. (1993) 207–216

    Google Scholar 

  2. Agraval R., Mannila H., Srikant R., Toivonen H., Verkamo A.I.: Fast Discovery of Association Rules. In: Fayyad, U.M., Pietetsky-Shapiro, G., Smyth, P., Uthurusamy, R. (eds.): Advances in Knowledge Discovery and Data Mining. AAAI, CA (1996) 307–328

    Google Scholar 

  3. Deogun J.S., Raghavan V.V., Sarkar A., Sever H.: Data Mining: Trends in Research and Development. In: Lin, T.Y., Cercone, N. (eds.): Rough Sets and Data Mining, Kluwer Academic Publishers (1997) 9–45

    Google Scholar 

  4. Kononenko, I., Bratko, I., Roskar, E.: Experiments in Automatic Learning of Medical Diagnostic Rules. Technical Report. Jozef Stefan Institute, Ljubljana, Yugoslavia (1984)

    Google Scholar 

  5. Kryszkiewicz, M.: Representative Associations Rules. In: Proc. of PAKDD’ 98. Melbourne, Australia. LNAI 1394. Research and Development in Knowledge Discovery and Data Mining, Springer-Verlag (1998) 198–209

    Google Scholar 

  6. Kryszkiewicz, M.: Fast Discovery of Representative Association Rules. In: Proc. of RSCTC’98. Warsaw, Poland. Rough Sets and Current Trends in Computing. Springer-Verlag. (1998 214–221

    Google Scholar 

  7. Kryszkiewicz, M.: Properties of Incomplete Information Systems in the Framework of Rough Sets: In Polkowski, L., Skowron, A. (eds.): Studies in Fuzziness and Soft Computing 18. Rough Sets in Knowledge Discovery 1. Physica-Verlag, Heidelberg (1998) 442–450

    Google Scholar 

  8. Kryszkiewicz, M., Rybinski H.: Incompleteness Aspects in the Rough Set Approach. In: Proc. of JCIS ‘98. Raleigh, USA. (1998) 371–374

    Google Scholar 

  9. Meo, R,., Psaila, G., Ceri, S.: A New SQL-Like Operator for Mining Association Rules. In: Proc. of the 22nd VLDB Conference. Mumbai (Bombay), India (1996)

    Google Scholar 

  10. Pawlak Z.: Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Vol. 9 (1991)

    Google Scholar 

  11. Quinlan J.R.: Induction of Decision Trees. In: Shavlik J. W., Dietterich T. G. (eds.): Readings in Machine Learning. Morgan Kauffman Publisher (1990) 57–69

    Google Scholar 

  12. Ragel A., Cremilleux B.: Treatment of Missing Values for Association Rules. In: Proc. of Second Pacific Asia Conference, PAKDD’ 98. Mebourne, Australia. LNAI 1394. Research and Development in Knowledge Discovery and Data Mining. Springer (1998) 258–270

    Google Scholar 

  13. Savasere, A, Omiecinski, E., Navathe, S.: An Efficient Algorithm for Mining Association Rules in Large Databases. In: Proc. of the 21st VLDB Conference. Zurich, Swizerland (1995) 432–444

    Google Scholar 

  14. Srikant, R., Agraval, R.: Mining Generalized Association Rules. In: Proc. of the 21st VLDB Conference. Zurich, Swizerland (1995) 407–419

    Google Scholar 

  15. Washio, T., Matsuura, H., Motoda, H.: Mining Association Rules for Estimation and Prediction. In: Proc. of PAKDD’ 98. Melbourne, Australia. LNAI 1394. Research and Development in Knowledge Discovery and Data Mining. Springer-Verlag (1998) 417–419

    Google Scholar 

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Author information

Authors and Affiliations

  1. Institute of Computer Science, Warsaw University of Technology, Nowowiejska 15/19, 00-665, Warsaw, Poland

    Marzena Kryszkiewicz

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  1. Marzena Kryszkiewicz
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Editors and Affiliations

  1. Department of Computer Science and Systems Engineering, Yamaguchi University, Tokiwa-Dai, 2557, Ube, 755, Japan

    Ning Zhong

  2. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Lizhu Zhou

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© 1999 Springer-Verlag Berlin Heidelberg

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Kryszkiewicz, M. (1999). Association Rules in Incomplete Databases. In: Zhong, N., Zhou, L. (eds) Methodologies for Knowledge Discovery and Data Mining. PAKDD 1999. Lecture Notes in Computer Science(), vol 1574. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48912-6_11

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  • DOI: https://doi.org/10.1007/3-540-48912-6_11

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65866-5

  • Online ISBN: 978-3-540-48912-2

  • eBook Packages: Springer Book Archive

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