Abstract
In the past, we proposed a Fast Updated FP-tree (FUFP-tree) structure to efficiently handle new transactions and to make the tree-update process become easy. In this paper, we propose the structure of prelarge trees to incrementally mine association rules based on the concept of pre-large itemsets. Due to the properties of pre-large concepts, the proposed approach does not need to rescan the original database until a number of new transactions have been inserted. Experimental results also show that the proposed approach has a good performance for incrementally handling new transactions.
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References
Agrawal, R., Imielinksi, T., Swami, A.: Mining association rules between sets of items in large database. In: The ACM SIGMOD Conference, pp. 207–216 (1993)
Agrawal, R., Srikant, R.: Fast algorithm for mining association rules. In: The International Conference on Very Large Data Bases, pp. 487–499 (1994)
Agrawal, R., Srikant, R.: Fast algorithm for mining association rules. In: The International Conference on Very Large Data Bases, pp. 487–499 (1994)
Agrawal, R., Srikant, R.: Mining sequential patterns. In: The Eleventh IEEE International Conference on Data Engineering, pp. 3–14 (1995)
Agrawal, R., Srikant, R., Vu, Q.: Mining association rules with item constraints. In: The Third International Conference on Knowledge Discovery in Databases and Data Mining, pp. 67–73 (1997)
Fukuda, T., Morimoto, Y., Morishita, S., Tokuyama, T.: Mining optimized association rules for numeric attributes. In: The ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, pp. 182–191 (1996)
Han, J., Fu, Y.: Discovery of multiple-level association rules from large database. In: The Twenty-first International Conference on Very Large Data Bases, pp. 420–431 (1995)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: The 2000 ACM SIGMOD International Conference on Management of Data, pp. 1–12 (2000)
Hong, T.P., Wang, C.Y., Tao, Y.H.: A new incremental data mining algorithm using pre-large itemsets. In: Intelligent Data Analysis, pp. 111–129 (2001)
Hong, T.P., Lin, C.W., Wu, Y.L.: Incrementally fast updated frequent pattern trees. Expert Systems with Applications (to appear)
Mannila, H.T., Verkamo, A.I.: Efficient algorithm for discovering association rules. In: The AAAI Workshop on Knowledge Discovery in Databases, pp. 181–192 (1994)
Park, J.S., Chen, M.S., Yu, P.S.: Using a hash-based method with transaction trimming for mining association rules. IEEE Transactions on Knowledge and Data Engineering, 812–825 (1997)
Zheng, Z., Kohavi, R., Mason, L.: Real world performance of association rule algorithms. In: The International Conference on Knowledge Discovery and Data Mining, pp. 401–406 (2001)
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Lin, CW., Hong, TP., Lu, WH., Chien, BC. (2008). Incremental Mining with Prelarge Trees. In: Nguyen, N.T., Borzemski, L., Grzech, A., Ali, M. (eds) New Frontiers in Applied Artificial Intelligence. IEA/AIE 2008. Lecture Notes in Computer Science(), vol 5027. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69052-8_18
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DOI: https://doi.org/10.1007/978-3-540-69052-8_18
Publisher Name: Springer, Berlin, Heidelberg
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