PARALLEL FUZZY FREQUENT ITEMSET MINING USING CELLULAR AUTOMATA
Author affiliations
DOI:
https://doi.org/10.15625/1813-9663/38/4/17462Keywords:
Frequent fuzzy itemsets, Cellular automata, Parallel mining.Abstract
Finding frequent fuzzy itemsets in operational quantitative databases is a significant challenge for fuzzy association rule mining in the context of data mining. If frequent fuzzy itemsets are detected, the decision-making process and formulating strategies in businesses will be made more precise. Because the characteristic of these data models is a large number of transactions and unlimited and high-speed productions. This leads to limitations in calculating the support for itemsets containing fuzzy attributes. As a result, mining using parallel processing techniques has emerged as a potential solution to the issue of slow availability. This study presents a reinforced technique for mining frequent fuzzy sets based on cellular learning automata (CLA). The results demonstrate that frequent set mining can be accomplished with less running time when the proposed method is compared to iMFFP and NPSFF methods.
Metrics
References
R. Agrawal, T. Imieli´nski, and A. Swami, Mining association rules between sets of items in large databases," in Proceedings of the 1993 ACM SIGMOD international conference on Management of data, 1993, pp. 207--216. DOI: https://doi.org/10.1145/170036.170072
R. Agrawal and J. C. Shafer, Parallel mining of association rules," IEEE Transactions on knowledge and Data Engineering, vol. 8, no. 6, pp. 962--969, 1996. DOI: https://doi.org/10.1109/69.553164
R. Agrawal and R. Srikant, Mining sequential patterns," in Proceedings of the eleventh international conference on data engineering. IEEE, 1995, pp. 3--14
R. Agrawal, R. Srikant et al., Fast algorithms for mining association rules," in Proc. 20th int.conf. very large data bases, VLDB, vol. 1215. Citeseer, 1994, pp. 487--499.
P. Arora, R. Chauhan, and A. Kush, Frequent itemsets from multiple datasets with fuzzy data," International Journal of Computer Theory and Engineering, vol. 3, no. 2, p. 255, 2011. DOI: https://doi.org/10.7763/IJCTE.2011.V3.313
H. Beigy and M. R. Meybodi, A mathematical framework for cellular learning automata," Advances in Complex Systems, vol. 7, no. 03n04, pp. 295--319, 2004. DOI: https://doi.org/10.1142/S0219525904000202
C. J. C. M. N. . S. J. M. Berzal, F., Tbar: An efficient method for association rule mining in relational databases," Data & Knowledge Engineering, vol. 37, no. 1, pp. 47--64, 2001. DOI: https://doi.org/10.1016/S0169-023X(00)00055-0
J.-S. Chen, F.-G. Chen, and J.-Y. Wang, Enhance the multi-level fuzzy association rules based on cumulative probability distribution approach," in 2012 13th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing. IEEE, 2012, pp. 89--94. DOI: https://doi.org/10.1109/SNPD.2012.36
M.-S. Chen, J. Han, and P. S. Yu, Data mining: an overview from a database perspective," IEEE Transactions on Knowledge and data Engineering, vol. 8, no. 6, pp. 866--883, 1996. DOI: https://doi.org/10.1109/69.553155
M. Esnaashari and M. Meybodi, A cellular learning automata based clustering algorithm for wireless sensor networks," Sensor Letters, vol. 6, no. 5, pp. 723--735, 2008. DOI: https://doi.org/10.1166/sl.2008.m146
M. Esnaashari and M. R. Meybodi, Dynamic point coverage in wireless sensor networks: A learning automata approach," in Computer Society of Iran Computer Conference. Springer, 2008, pp. 758--762. DOI: https://doi.org/10.1007/978-3-540-89985-3_97
Esnaashari, Mehdi and Meybodi, Mohammad Reza, Irregular cellular learning automata," IEEE transactions on cybernetics, vol. 45, no. 8, pp. 1622--1632, 2014. DOI: https://doi.org/10.1109/TCYB.2014.2356591
P. Fournier-Viger, A. Gomariz, T. Gueniche, A. Soltani, C.-W. Wu, and V. S. Tseng, Spmf: a java open-source pattern mining library," The Journal of Machine Learning Research, vol. 15, no. 1, pp. 3389--3393, 2014.
T.-P. Hong, C.-S. Kuo, and S.-L. Wang, A fuzzy aprioritid mining algorithm with reduced computational time," Applied Soft Computing, vol. 5, no. 1, pp. 1--10, 2004. DOI: https://doi.org/10.1016/j.asoc.2004.03.009
T.-P. Hong, Y.-C. Lee, and M.-T. Wu, An effective parallel approach for genetic-fuzzy data mining," Expert Systems with Applications, vol. 41, no. 2, pp. 655--662, 2014. DOI: https://doi.org/10.1016/j.eswa.2013.07.090
T.-P. Hong, C.-W. Lin, and T.-C. Lin, The mffp-tree fuzzy mining algorithm to discover complete linguistic frequent itemsets," Computational Intelligence, vol. 30, no. 1, pp. 145--166, 2014. DOI: https://doi.org/10.1111/j.1467-8640.2012.00467.x
T.-P. Hong, C.-W. Lin, and Y.-L. Wu, Incrementally fast updated frequent pattern trees," Expert Systems with Applications, vol. 34, no. 4, pp. 2424--2435, 2008. DOI: https://doi.org/10.1016/j.eswa.2007.04.009
T.-P. Hong, K.-Y. Lin, and B.-C. Chien, Mining fuzzy multiple-level association rules from quantitative data," Applied Intelligence, vol. 18, no. 1, pp. 79--90, 2003. DOI: https://doi.org/10.1023/A:1020991105855
T.-P. Hong, C.-H. Wu et al., An improved weighted clustering algorithm for determination of application nodes in heterogeneous sensor networks," 2011.
K. Hu, Y. Lu, L. Zhou, and C. Shi, Integrating classification and association rule mining: A concept lattice framework," in International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing. Springer, 1999, pp. 443--447. DOI: https://doi.org/10.1007/978-3-540-48061-7_53
R. Jain and W. Stallings, Comments on" fuzzy set theory versus bayesian statistics"," IEEE Transactions on Systems, Man, and Cybernetics, vol. 8, no. 4, pp. 332--333, 1978. DOI: https://doi.org/10.1109/TSMC.1978.4309962
C. Z. Janikow, Fuzzy decision trees: issues and methods," IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 28, no. 1, pp. 1--14, 1998. DOI: https://doi.org/10.1109/3477.658573
B. Lent, A. Swami, and J. Widom, Clustering association rules," in Proceedings 13th International Conference on Data Engineering. IEEE, 1997, pp. 220--231.
C.-W. Lin, T.-P. Hong, Y.-F. Chen, T.-C. Lin, and S.-T. Pan, An integrated mffp-tree algorithm for mining global fuzzy rules from distributed databases." J. Univers. Comput. Sci., vol. 19, no. 4, pp. 521--538, 2013.
C.-W. Lin, T.-P. Hong, and W.-H. Lu, The pre-fufp algorithm for incremental mining," Expert Systems with Applications, vol. 36, no. 5, pp. 9498--9505, 2009. DOI: https://doi.org/10.1016/j.eswa.2008.03.014
Lin, Chun-Wei and Hong, Tzung-Pei and Lu, Wen-Hsiang, Linguistic data mining with fuzzy fp-trees," Expert Systems with Applications, vol. 37, no. 6, pp. 4560--4567, 2010. DOI: https://doi.org/10.1016/j.eswa.2009.12.052
F. Liu, Z. Lu, and S. Lu, Mining association rules using clustering," Intelligent Data Analysis, vol. 5, no. 4, pp. 309--326, 2001. DOI: https://doi.org/10.3233/IDA-2001-5403
J. S. Park, M.-S. Chen, and P. S. Yu, Using a hash-based method with transaction trimming for mining association rules," IEEE transactions on knowledge and data engineering, vol. 9, no. 5, pp. 813--825, 1997. DOI: https://doi.org/10.1109/69.634757
K. S. Prabha and R. Lawrance, Mining fuzzy frequent item set using compact frequent pattern (cfp) tree algorithm," Data Mining and Knowledge Engineering, vol. 4, no. 7, pp. 365--369, 2012.
P. Pulkkinen and H. Koivisto, A dynamically constrained multi objective genetic fuzzy system for regression problems," IEEE Transactions on Fuzzy Systems, vol. 18, no. 1, pp. 161--177, 2009. DOI: https://doi.org/10.1109/TFUZZ.2009.2038712
M. Rezapoor Mirsaleh and M. R. Meybodi, A new memetic algorithm based on cellular learning automata for solving the vertex coloring problem," Memetic Computing, vol. 8, no. 3, pp. 211--222, 2016. DOI: https://doi.org/10.1007/s12293-016-0183-4
R. Senge and E. H¨ullermeier, Top-down induction of fuzzy pattern trees," IEEE Transactions on Fuzzy Systems, vol. 19, no. 2, pp. 241--252, 2010. DOI: https://doi.org/10.1109/TFUZZ.2010.2093532
R. Srikant and R. Agrawal, Mining sequential patterns: Generalizations and performance improvements," in International conference on extending database technology. Springer, 1996, pp.1--17. DOI: https://doi.org/10.1007/BFb0014140
Y. G. Sucahyo and R. P. Gopalan, Building a more accurate classifier based on strong frequent patterns," in Australasian Joint Conference on Artificial Intelligence. Springer, 2004, pp. 1036--1042. DOI: https://doi.org/10.1007/978-3-540-30549-1_98
T. T. Tran, T. N. Nguyen, T. T. Nguyen, G. L. Nguyen, and C. N. Truong, A fuzzy association rules mining algorithm with fuzzy partitioning optimization for intelligent decision systems," International Journal of Fuzzy Systems, pp. 1--14, 2022. DOI: https://doi.org/10.1007/s40815-022-01308-w
X.-Z. Wang, L.-C. Dong, and J.-H. Yan, Maximum ambiguity-based sample selection in fuzzy decision tree induction," IEEE Transactions on Knowledge and Data Engineering, vol. 24, no. 8, pp. 1491--1505, 2011. DOI: https://doi.org/10.1109/TKDE.2011.67
M. Wo´zniak and B. Krawczyk, Combined classifier based on feature space partitioning," International Journal of Applied Mathematics and Computer Science, vol. 22, no. 4, pp. 855--866, 2012. DOI: https://doi.org/10.2478/v10006-012-0063-0
L. A. Zadeh, Fuzzy sets," Information and control, vol. 8, no. 3, pp. 338--353, 1965. DOI: https://doi.org/10.1016/S0019-9958(65)90241-X
M. F. Zaman and H. Hirose, Classification performance of bagging and boosting type ensemble methods with small training sets," New Generation Computing, vol. 29, no. 3, pp. 277--292, 2011. DOI: https://doi.org/10.1007/s00354-011-0303-0
Downloads
Published
How to Cite
Issue
Section
License
1. We hereby assign copyright of our article (the Work) in all forms of media, whether now known or hereafter developed, to the Journal of Computer Science and Cybernetics. We understand that the Journal of Computer Science and Cybernetics will act on my/our behalf to publish, reproduce, distribute and transmit the Work.2. This assignment of copyright to the Journal of Computer Science and Cybernetics is done so on the understanding that permission from the Journal of Computer Science and Cybernetics is not required for me/us to reproduce, republish or distribute copies of the Work in whole or in part. We will ensure that all such copies carry a notice of copyright ownership and reference to the original journal publication.
3. We warrant that the Work is our results and has not been published before in its current or a substantially similar form and is not under consideration for another publication, does not contain any unlawful statements and does not infringe any existing copyright.
4. We also warrant that We have obtained the necessary permission from the copyright holder/s to reproduce in the article any materials including tables, diagrams or photographs not owned by me/us.
