Abstract
Mining association rules is to discover customer purchasing behaviors from a transaction database, such that the quality of business decision can be improved. However, the size of the transaction database can be very large. It is very time consuming to find all the association rules from a large database, and users may be only interested in some information. Hence, a data mining language needs to be provided such that users can query only interesting knowledge to them from a large database of customer transactions. In this paper, a data mining language is presented. From the data mining language, users can specify the interested items and the criteria of the association rules to be discovered. Also, the efficient data mining techniques are proposed to extract the association rules according to the user requirements.
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References
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Show-Jane Yen and A.L.P. Chen. “A Graph-Based Approach for Discovering Various Types of Association Rules,” IEEE Transactions on Knowledge and Data Engineering, Vol. 13, No. 5, pp. 839–845, 2001.
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© 2002 Springer-Verlag Berlin Heidelberg
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Yen, SJ., Lee, YS. (2002). Mining Interesting Association Rules: A Data Mining Language. In: Chen, MS., Yu, P.S., Liu, B. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2002. Lecture Notes in Computer Science(), vol 2336. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-47887-6_16
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DOI: https://doi.org/10.1007/3-540-47887-6_16
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