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Published Articles >> Table of Contents >> Abstract
June 2004 (Vol. 16, No. 6)
pp. 774-778
Data Structure for Association Rule Mining: T-Trees and P-Trees
Frans Coenen
Paul Leng
Shakil Ahmed
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2004.8
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| Abstract |
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Two new structures for Association Rule Mining (ARM), the T-tree, and the P-tree, together with associated algorithms, are described. The authors demonstrate that the structures and algorithms offer significant advantages in terms of storage and execution time.
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 References
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[1] R. Agrawal and R. Srikant, Fast Algorithms for Mining Association Rules Proc. 20th Very Large Databses (VLDB) Conf., pp. 487-499, 1994.
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[3] S. Brin, R. Motwani, J. Ullman, and S. Tsur, Dynamic Itemset Counting and Implication Rules for Market Basket Data Proc. ACM SIGMOD, Int'l Conf. Management of Data, pp. 255-264, 1997.
[4] J. Han, J. Pei, and Y. Yiwen, Mining Frequent Patterns Without Candidate Generation Proc. ACM-SIGMOD Int'l Conf. Management of Data, pp. 1-12, 2000.
[5] Quest project,http://www.almaden.ibm.com/csquest/, IBM Almaden Research Center, 2004.
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[7] M.J. Zaki, S. Parthasarathy, M. Ogihara, and W. Li, New algorithms for Fast Discovery of Association Rules Proc. Third Int'l Conf. Knowledge Discovery and Data Mining, 1997.
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 Additional Information
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Index Terms- Association Rule Mining, T-tree, P-tree.
Citation:
Frans Coenen, Paul Leng, Shakil Ahmed,
"Data Structure for Association Rule Mining: T-Trees and P-Trees,"
IEEE Transactions on Knowledge and Data Engineering,
vol. 16,
no. 6,
pp. 774-778,
Jun.,
2004
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