Abstract
In this paper, an incremental mining algorithm is proposed for efficiently maintaining the discovered high utility itemsets based on the pre-large concept. It first partitions itemsets into nine cases according to whether they are large (high), pre-large or small transaction-weighted utilization in the original database and in the inserted transactions. Each part is then performed by its own procedure. Experimental results also show that the designed incremental high utility mining algorithm has better performance than the bach one for handling inserted transactions.
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References
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: The International Conference on Very Large Data Bases, pp. 487–499 (1994)
Berzal, F., Cubero, J.C., MarÃn, N., Serrano, J.M.: TBAR: An efficient method for association rule mining in relational databases. Data and Knowledge Engineering 37, 47–64 (2001)
Chan, R., Yang, Q., Shen, Y.D.: Mining high utility itemsets. In: IEEE International Conference on Data Mining, pp. 19–26 (2003)
Chen, M.S., Han, J., Yu, P.S.: Data mining: an overview from a database perspective. IEEE Transactions on Knowledge and Data Engineering 8, 866–883 (1996)
Cheung, D.W., Han, J., Ng, V.T., Wong, C.Y.: Maintenance of discovered association rules in large databases: an incremental updating technique. In: The International Conference on Data Engineering, pp. 106–114 (1996)
Hong, T.P., Wang, C.Y., Tao, Y.H.: A new incremental data mining algorithm using pre-large itemsets. Intelligent Data Analysis 5, 111–129 (2001)
Hong, T.P., Wang, C.Y.: Maintenance of association rules using pre-large itemsets. In: Intelligent Databases: Technologies and Applications, pp. 44–60 (2006)
IBM quest data mining project, Quest synthetic data generation code, http://www.almaden.ibm.com/cs/quest/syndata.html
Lin, C.W., Hong, T.P., Lu, W.H.: The Pre-FUFP algorithm for incremental mining. Expert Systems with Applications 36, 9498–9505 (2009)
Lin, C.W., Lan, G.C., Hong, T.P.: An incremental mining algorithm for high utility itemsets. Expert Systems with Applications 39, 7173–7180 (2012)
Liu, Y., Liao, W.-K., Choudhary, A.K.: A Two-Phase Algorithm for Fast Discovery of High Utility Itemsets. In: Ho, T.-B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 689–695. Springer, Heidelberg (2005)
Yao, H., Hamilton, H.J.: Mining itemset utilities from transaction databases. Data and Knowledge Engineering 59, 603–626 (2006)
Yao, H., Hamilton, H.J., Butz, C.J.: A foundational approach to mining itemset utilities from databases. In: The SIAM International Conference on Data Mining, pp. 211–225 (2004)
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Lin, CW., Hong, TP., Lan, GC., Wong, JW., Lin, WY. (2013). Mining High Utility Itemsets Based on the Pre-large Concept. In: Chang, RS., Jain, L., Peng, SL. (eds) Advances in Intelligent Systems and Applications - Volume 1. Smart Innovation, Systems and Technologies, vol 20. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35452-6_26
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DOI: https://doi.org/10.1007/978-3-642-35452-6_26
Publisher Name: Springer, Berlin, Heidelberg
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