Optimization of Association Rule Using Ant Colony Optimization (ACO) Approach
DOI:
https://doi.org/10.31185/wjcms.190Keywords:
Data Mining, Association Rule Mining (ARM), Apriori Algorithm, Ant Colony Optimization (ACO), FP-Growth.Abstract
The Apriori algorithm creates all possible association rules between items in the database using the Association Rule Mining and Apriori Algorithm. Using Ant Colony Optimization, a new algorithm is proposed for improving association rule mining results. Using ant colony behaviour as a starting point, an optimization of ant colonies (ACO) is developed. The Apriori algorithm creates association rules. Determine the weakest rule set and reduce the association rules to find rules of higher quality than apriori based on the Ant Colony algorithm's threshold value. Through optimization and improvement of rules generated for ACO, the proposed research work aims to reduce the scanning of datasets.
References
F. Chiclana, R. Kumar, M. Mittal, M. Khari, J. M. Chatterjee, and S. W. Baik, "ARM–AMO: An efficient association rule mining algorithm based on animal migration optimization," Knowledge-Based Systems, vol. 154, pp. 68-80, 2018. DOI: https://doi.org/10.1016/j.knosys.2018.04.038
P. H. Thong, "A novel automatic picture fuzzy clustering method based on particle swarm optimization and picture composite cardinality," Knowledge-Based Systems, vol. 109, pp. 48-60, 2016. DOI: https://doi.org/10.1016/j.knosys.2016.06.023
B. Vo, S. Pham, T. Le, and Z.-H. Deng, "A novel approach for mining maximal frequent patterns," Expert Systems with Applications, vol. 73, pp. 178-186, 2017. DOI: https://doi.org/10.1016/j.eswa.2016.12.023
J. Han, M. Kamber, and D. Mining, "Concepts and techniques," Morgan kaufmann, vol. 340, pp. 94104-3205, 2006.
P. Rani, S. Verma, S. P. Yadav, B. K. Rai, M. S. Naruka, and D. Kumar, "Simulation of the Lightweight Blockchain Technique Based on Privacy and Security for Healthcare Data for the Cloud System," International Journal of E-Health and Medical Communications (IJEHMC), vol. 13, no. 4, pp. 1-15, 2022. DOI: https://doi.org/10.4018/IJEHMC.309436
N. Hussain, P. Rani, N. Kumar, and M. G. Chaudhary, "A deep comprehensive research architecture, characteristics, challenges, issues, and benefits of routing protocol for vehicular ad-hoc networks," International Journal of Distributed Systems and Technologies (IJDST), vol. 13, no. 8, pp. 1-23, 2022. DOI: https://doi.org/10.4018/IJDST.307900
G. S. Linoff and M. J. Berry, Data mining techniques: for marketing, sales, and customer relationship management. John Wiley & Sons, 2011.
W. Ceusters, "Medical natural language understanding as a supporting technology for data mining in healthcare," Studies In Fuzziness And Soft Computing, vol. 60, pp. 41-71, 2001.
J. Pesce, "Stanching hospitals' financial hemorrhage with information technology," Health Management Technology, vol. 24, no. 8, pp. 12-12, 2003.
A. C. Tessmer, "What to learn from near misses: an inductive learning approach to credit risk assessment," Decision Sciences, vol. 28, no. 1, pp. 105-120, 1997. DOI: https://doi.org/10.1111/j.1540-5915.1997.tb01304.x
M. Dorigo, V. Maniezzo, and A. Colorni, "Ant system: optimization by a colony of cooperating agents," IEEE transactions on systems, man, and cybernetics, part b (cybernetics), vol. 26, no. 1, pp. 29-41, 1996. DOI: https://doi.org/10.1109/3477.484436
M. Dorigo and L. M. Gambardella, "Ant colony system: a cooperative learning approach to the traveling salesman problem," IEEE Transactions on evolutionary computation, vol. 1, no. 1, pp. 53-66, 1997. DOI: https://doi.org/10.1109/4235.585892
M. Dorigo, M. Birattari, and T. Stutzle, "Ant colony optimization," IEEE computational intelligence magazine, vol. 1, no. 4, pp. 28-39, 2006. DOI: https://doi.org/10.1109/MCI.2006.329691
R. C. Eberhart, Y. Shi, and J. Kennedy, Swarm Intelligence (Morgan Kaufmann series in evolutionary computation). Morgan Kaufmann Publishers, 2001.
A. Engelbrecht, "Fundamentals of Computational Swarm Intelligence. John Wiley & Sons, Chichester, UK," 2005.
A. P. Engelbrecht, Computational intelligence: an introduction. John Wiley & Sons, 2007. DOI: https://doi.org/10.1002/9780470512517
N. Kumar, P. Rani, V. Kumar, S. V. Athawale, and D. Koundal, "THWSN: Enhanced energy-efficient clustering approach for three-tier heterogeneous wireless sensor networks," IEEE Sensors Journal, vol. 22, no. 20, pp. 20053-20062, 2022. DOI: https://doi.org/10.1109/JSEN.2022.3200597
Y. Yaginuma, "High-performance data mining system," Fujitsu Scientific and Technical Journal, vol. 36, no. 2, pp. 201-210, 2000.
C. Grosan, A. Abraham, and M. Chis, "Swarm intelligence in data mining," in Swarm Intelligence in Data Mining: Springer, 2006, pp. 1-20. DOI: https://doi.org/10.1007/978-3-540-34956-3_1
C. T. Hardin and J. S. Usher, "Facility layout using swarm intelligence," in Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005., 2005: IEEE, pp. 424-427.
S. Lorpunmanee, M. N. Sap, A. H. Abdullah, and C. Chompoo-inwai, "An Ant Colony Optimization for Dynamic JobScheduling in Grid Environment," International Journal of Computer and Information Engineering, vol. 1, no. 5, pp. 1343-1350, 2007.
B. Chakraborty, "Feature subset selection by particle swarm optimization with fuzzy fitness function," in 2008 3rd international conference on intelligent system and knowledge engineering, 2008, vol. 1: IEEE, pp. 1038-1042. DOI: https://doi.org/10.1109/ISKE.2008.4731082
K. M. Sim and W. H. Sun, "Multiple ant-colony optimization for network routing," in First International Symposium on Cyber Worlds, 2002. Proceedings., 2002: IEEE, pp. 277-281.
X. Tan, X. Zhuo, and J. Zhang, "Ant colony system for optimizing vehicle routing problem with time windows (VRPTW)," in International Conference on Intelligent Computing, 2006: Springer, pp. 33-38. DOI: https://doi.org/10.1007/11816102_4
E. Salari and K. Eshghi, "An ACO algorithm for graph coloring problem," in 2005 ICSC Congress on computational intelligence methods and applications, 2005: IEEE, p. 5 pp.
M.-E. Lee, S.-H. Kim, W.-H. Cho, S.-Y. Park, and J.-S. Lim, "Segmentation of brain MR images using an ant colony optimization algorithm," in 2009 Ninth IEEE international conference on bioinformatics and bioengineering, 2009: IEEE, pp. 366-369. DOI: https://doi.org/10.1109/BIBE.2009.58
C.-J. Lin, C. Chen, and C. Lee, "Classification and medical diagnosis using wavelet-based fuzzy neural networks," International Journal of Innovative Computing, Information and Control, vol. 4, no. 3, pp. 735-748, 2008.
R. S. Parpinelli, H. S. Lopes, and A. A. Freitas, "An ant colony based system for data mining: applications to medical data," in Proceedings of the 3rd annual conference on genetic and evolutionary computation, 2001: Citeseer, pp. 791-797.
R. Duda, P. Hart, and D. Stork, "Pattern classification, edition wiley interscience," New York, 2001.
S.-S. Weng and Y.-H. Liu, "Mining time series data for segmentation by using Ant Colony Optimization," European Journal of Operational Research, vol. 173, no. 3, pp. 921-937, 2006. DOI: https://doi.org/10.1016/j.ejor.2005.09.001
B. C. Mohan and R. Baskaran, "A survey: Ant Colony Optimization based recent research and implementation on several engineering domain," Expert Systems with Applications, vol. 39, no. 4, pp. 4618-4627, 2012. DOI: https://doi.org/10.1016/j.eswa.2011.09.076
J.-L. Deneubourg, S. Goss, N. Franks, A. Sendova-Franks, C. Detrain, and L. Chrétien, "The dynamics of collective sorting robot-like ants and ant-like robots," in From animals to animats: proceedings of the first international conference on simulation of adaptive behavior, 1991, pp. 356-365.
N. Kumar, P. Rani, V. Kumar, P. K. Verma, and D. Koundal, "TEEECH: Three-Tier Extended Energy Efficient Clustering Hierarchy Protocol for Heterogeneous Wireless Sensor Network," Expert Systems with Applications, vol. 216, p. 119448, 2023. DOI: https://doi.org/10.1016/j.eswa.2022.119448
Downloads
Published
Issue
Section
License
Copyright (c) 2023 Roni La’biran, Muhammad Kristiawan
This work is licensed under a Creative Commons Attribution 4.0 International License.
