An Efficient Algorithm for Mining Closed Patterns on Dynamic Quantitative Databases
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Abstract
In recent years, the problem of mining frequent weighted utility patterns (FWUPs)-a variant of frequent patterns-has attracted significant attention from the research community. Several algorithms have been proposed to address this problem using quantitative databases in which item weights remain fixed across the entire database. However, in real-world scenarios, various factors can affect item weights, and these weights may change over time to better reflect actual conditions. Moreover, traditional FWUP mining methods are often resource-intensive, as the number of generated patterns is highly sensitive to the chosen mining threshold.
In this paper, we propose the problem of mining frequent weighted utility closed patterns (FWUCPs), a condensed representation from which all FWUPs can be derived, from dynamic quantitative databases, where item weights can be adjusted. The proposed algorithm, nFWUCP, leverages the WUNList structure, an extension of the N-list structure, to efficiently discover FWUCPs. Experimental results demonstrate that our approach achieves superior execution performance compared to the current state-of- the-art algorithm.
Keywords
data mining, frequent weighted utility patterns, closed patterns, dynamic quantitative databases
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References
J. Han, J. Pei and Y. Yin, "Mining frequent patterns without candidate generation," in The 2000 ACM SIGMOD International Conference on Management of Data, 2000.
M. J. Zaki, "Scalable algorithms for association mining," IEEE Transactions on Knowledge and Data Engineering, vol. 12, no. 3, pp. 372-390, 2000.
Z. Deng, Z. Wang and J. Jiang, "A new algorithm for fast mining frequent itemsets using N- lists," Science China Information Sciences, vol. 55, no. 9, pp. 2008-2030, Jul. 2012.
B. Vo, T. Le, F. Coenen and T. Hong, "Mining frequent itemsets using the N-list and subsume concepts," International Journal of Machine Learning and Cybernetics, vol.
7, no. 2, pp. 253-265, Apr. 2016
N. Pasquier, Y. Bastide, R. Taouil and L. Lakhal, "Discovering frequent closed itemsets for association rules," in Database Theory - ICDT'99, Jerusalem, Israel, 1999.
M. J. Zaki and C.-J. Hsiao, "CHARM: An efficient algorithm for closed association rule mining," Technical Report 99, 1999.
C. Lucchese, S. Orlando and R. Perego, "Fast and memory efficient mining of frequent closed itemsets," IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 1, pp. 21-36, 2006.
T. Le and B. Vo, "An N-list-based algorithm for mining frequent closed patterns," Expert Systems with Applications, vol. 42, no. 19, pp. 6648-6657, Nov. 2015.
N. Aryabarzan and B. Minaei-Bidgoli, "Neclatclosed: A vertical algorithm for mining frequent closed itemsets," Expert Systems with Applications, p. 174: 114738, 2021.
M. S. Khan, M. Muyeba and F. Coenen, "A weighted utility framework for mining association rules," in Computer Modeling and Simulation, 2008., 2008.
H. Nguyen, B. Vo, M. T. H. Nguyen and T. Hong, "MBiS: an efficient method for mining frequent weighted utility itemsets from quantitative databases," Journal of Computer
Science and Cybernetics, vol. 31, 2015.
H. Bui, B. Vo and H. Nguyen, "WUN-Miner: A new method for mining frequent weighted utility itemsets," in The 2016 IEEE Conference on System, Man, and Cybernetics (SMC 2016), Budapest, 2016.
H. Nguyen, N. Le, H. Bui and T. Le, "A new approach for efficiently mining frequent weighted utility patterns," Applied Intelligence, pp. 53(1), 121-140, 2023.
H. Nguyen, N. Le, H. Bui and T. Le, "Mining frequent weighted utility patterns with dynamic weighted items from quantitative databases," Applied Intelligence, pp. 53(16), 19629-19646, 2023.
B. Vo, B. Le and J. J. Jung, "A tree-based approach for mining frequent weighted utility itemsets," in Computational Collective Intelligence. Technologies and Applications. Springer, 2012, pp. 114-123.
H. Nguyen, T. Le, M. Nguyen, P. Fournier-Viger, V. S. Tseng and B. Vo, "Mining frequent weighted utility itemsets in hierarchical quantitative databases," Knowledge-Based Systems, vol. 237, p. 107709, 2022.
J. Pei, J. Han and R. Mao, "CLOSET: An efficient algorithm for mining frequent closet itemsets," in 2000 ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, Dallas, Texas, USA, May. 2000.
J. Wang, J. Han and J. Pei, "CLOSET+ : Searching for best strategies for mining frequent closed itemsets," in Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August. 2003