Improvised Swarm Based Discrete Data Mining Approach for High Utility Item Sets


  • Raja Rao Budaraju Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur 522302, Andhra Pradesh, India.
  • Sastry Kodanda Rama Jammalamadaka Department of Electronics and Computer Science, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur 522302, Andhra Pradesh, India.


High Utility Itemset, discrete, Improved discrete cuckoo search


Data mining techniques uncover valuable patterns hidden inside extensive databases to assist decision support systems in different practical applications. Association rule mining analyzes the transaction database to recognize patterns and provides insights into client behavior. Frequent itemset mining (FIM) detects a group of items that are commonly purchased together. A significant limitation of FIM is its disregard for the item's significance. The significance of an item is crucial in a practical application. Hence, it is imperative to identify the critical set of items that yields substantial profits, referred to as the HUIM (High-Utility Itemset Mining) problem. Various techniques may be employed to identify high utility itemsets from a transaction database. The HUIM approaches that employ the Utility list are relatively new and exhibit superior performance in terms of memory consumption and execution time. Primary constraint of these algorithms is the execution of expensive utility list join operations. This work presents a highly efficient optimization approach based on swarm intelligence for addressing the HUIM problem. Furthermore, the suggested method's execution time is assessed. Additionally, it is compared to relevant and advanced current approaches. Extensive tests were carried out on accessible benchmark datasets demonstrate that the suggested swarm-based methodology outperforms state-of-the-art approaches.


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J.M. Luna, P. Fournier-Viger, S. Ventura, Frequent itemset mining: A 25 years review, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 9 (6) (2019).

C.C. Aggarwal, J. Han, Frequent Pattern Mining. Springer Publishing Company, 2014. ISBN: 978-3-319-07821-2.

P. Fournier-Viger, J.C.-W. Lin, R. Nkambou, B. Vo, V.S. Tseng, High-Utility Pattern Mining: Theory, Algorithms and Applications, 1st edition., Springer Publishing Company, 2019.

W. Gan, J.C. Lin, J. Zhang, P. Fournier-Viger, H. Chao, P.S. Yu, Fast utility mining on sequence data, IEEE Transaction on Cybernetics 51 (2) (2021) 487– 500.

P. Fournier-Viger, C. Wu, S. Zida, V.S. Tseng, FHM: faster high-utility itemset mining using estimated utility co-occurrence pruning, in: Proceedings of the 21st International Symposium on Foundations of Intelligent Systems, ISMIS 2014, Roskilde, Denmark, June 25–27, 2014. Proceedings, volume 8502, Springer, 2014, pp. 83–92.

P. Fournier-Viger, J.C.-W. Lin, R. Nkambou, B. Vo, V.S. Tseng, High-Utility Pattern Mining: Theory, Algorithms and Applications, 1st edition., Springer Publishing Company, 2019.

W. Gan, J.C. Lin, P. Fournier-Viger, H. Chao, P.S. Yu, HUOPM: high-utility occupancy pattern mining, IEEE Transactions on Cybernetics 50 (3) (2020) 1195–1208.

Y. Liu, W. Liao, A.N. Choudhary, A two-phase algorithm for fast discovery of high utility itemsets, in: Proceedings of the 9th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2005, Hanoi, Vietnam, May 18–20, 2005, Proceedings, volume 3518, Springer, 2005, pp. 689–695.

C.F. Ahmed, S.K. Tanbeer, B. Jeong, Y. Lee, Efficient tree structures for high utility pattern mining in incremental databases, IEEE Transaction on Data and Knowledge Engineering 21 (12) (2009) 1708–1721.

V.S. Tseng, B. Shie, C. Wu, P.S. Yu, Efficient algorithms for mining high utility itemsets from transactional databases, IEEE Transaction on Data and Knowledge Engineering 25 (8) (2013) 1772–1786.

S. Kannimuthu, K. Premalatha, Discovery of high utility itemsets using genetic algorithm with ranked mutation, Applied Artificial Intelligence 28 (4) (2014) 337–359.

J.C. Lin, L. Yang, P. Fournier-Viger, T. Hong, M. Voznák, A binary PSO approach to mine high-utility itemsets, Soft Computing 21 (17) (2017) 5103 5121.

Y. Liu W.K. Liao A. Choudhary A Two-Phase Algorithm for Fast Discovery of High Utility Itemsets 2005 Springer-Verlag 689 695 10.1007/11430919_79.

Le, B., Nguyen, H., & Vo, B. (2011). An efficient strategy for mining high utility itemsets. International Journal of Intelligent Information and Database Systems, 5(2), 164–176

Tseng, V. S., Wu, C. W., Shie, B. E., & Yu, P. S. (2010). UP-Growth: An efficient algorithm for high utility itemset mining. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 253–262.

Qu, J.-F., Fournier-Viger, P., Liu, M., Hang, B., & Hu, C. (2023). Mining high utility itemsets using prefix trees and utility vectors. IEEE Transactions on Knowledge and Data Engineering, 1–14.

Liu, M., & Qu, J. (2012). Mining high utility itemsets without candidate generation. Proceedings of the 21st ACM International Conference on Information and Knowledge Management - CIKM ’12, 55.

Fournier-Viger, P., Wu, C.-W., Zida, S., & Tseng, V. S. (2014). FHM: Faster High-Utility itemset mining using estimated utility co-occurrence pruning. In In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Vol. 8502 LNAI (pp. 83–92).

Krishnamoorthy, S. (2015). Pruning strategies for mining high utility itemsets. Expert Systems with Applications, 42(5), 2371–2381.

Duong, Q.-H.-H., Fournier-Viger, P., Ramampiaro, H., Nørvåg, K., & Dam, T.-L.-L. (2018). Efficient high utility itemset mining using buffered utility-lists. Applied Intelligence, 48(7), 1859–1877.

Wu, P., Niu, X., Fournier-Viger, P., Huang, C., & Wang, B. (2022). UBP-Miner: An efficient bit based high utility itemset mining algorithm. Knowledge-Based Systems, 248, Article 108865.

Zida, S., Fournier-Viger, P., Lin, J.-C.-W., Wu, C.-W., & Tseng, V. S. (2017). EFIM: A fast and memory efficient algorithm for high-utility itemset mining. Knowledge and Information Systems, 51(2), 595–625.

Nguyen, L. T. T., Nguyen, P., Nguyen, T. D. D., Vo, B., Fournier-Viger, P., & Tseng, V. S. (2019). Mining high-utility itemsets in dynamic profit databases. Knowledge-Based Systems, 175, 130–144.

Lan, W., Lin, J.-C.-W., Fournier-Viger, P., Chao, H.-C., Tseng, V. S., & Yu, P. S. (2021). A survey of Utility-Oriented pattern mining. IEEE Transactions on Knowledge and Data Engineering, 33(4), 1306–1327.

Vo, B., Nguyen, L. T. T., Bui, N., Nguyen, T. D. D., Huynh, V.-N., & Hong, T.-P. (2020). An efficient method for mining closed potential High-Utility itemsets. IEEE Access, 8, 31813–31822.

Duong, H., Hoang, T., Tran, T., Truong, T., Le, B., & Fournier-Viger, P. (2022). Efficient algorithms for mining closed and maximal high utility itemsets. Knowledge-Based Systems, 257, Article 109921.

Ahmed, U., Lin, J.-C.-W., Srivastava, G., Yasin, R., & Djenouri, Y. (2021). An evolutionary model to mine high expected utility patterns from uncertain databases. IEEE Transactions on Emerging Topics in Computational Intelligence, 5(1), 19–28.

Tung, N. T., Nguyen, L. T. T., Nguyen, T. D. D., Fourier-Viger, P., Nguyen, N.-T., & Vo, B. (2022a). Efficient mining of cross-level high-utility itemsets in taxonomy quantitative databases. Information Sciences, 587, 41–62.

Srivastava, G., Lin, J.-C.-W., Pirouz, M., Li, Y., & Yun, U. (2021). A Pre-Large WeightedFusion system of sensed High-Utility patterns. IEEE Sensors Journal, 21(14), 15626–15634.

Attuluri, S., Ramesh, M. Multi-objective discrete harmony search algorithm for privacy preservation in cloud data centers. Int. j. inf. tecnol. (2023).

Hazzazi, Mohammad Mazyad, Sasidhar Attuluri, Zaid Bassfar, and Kireet Joshi. 2023. "A Novel Cipher-Based Data Encryption with Galois Field Theory" Sensors 23, no. 6: 3287.

R. R. Budaraju and O. S. Nagesh, "Multi-Level Image Thresholding Using Improvised Cuckoo Search Optimization Algorithm," 2023 3rd International Conference on Intelligent Technologies (CONIT), Hubli, India, 2023, pp. 1-7, doi: 10.1109/CONIT59222.2023.10205744.

Hazzazi, Mohammad Mazyad, Raja Rao Budaraju, Zaid Bassfar, Ashwag Albakri, and Sanjay Mishra. 2023. "A Finite State Machine-Based Improved Cryptographic Technique" Mathematics 11, no. 10: 2225.

Thirugnansambandam, K., Bhattacharyya, D., Frnda, J., Anguraj, D. K., & Nedoma, J. (2021). Augmented Node Placement Model in t-WSN Through Multi objective Approach. Comput. Mater. Contin. CMC Tech Sci. Press, 69, 3629-3644.

Thirugnanasambandam, K., Raghav, R. S., Anguraj, D. K., Saravanan, D., & Janakiraman, S. (2021). Multi-objective Binary Reinforced Cuckoo Search Algorithm for Solving Connected Coverage target based WSN with Critical Targets. Wireless Personal Communications, 121(3), 2301-2325.

Thirugnanasambandam, K.; Ramalingam, R.; Mohan, D.; Rashid, M.; Juneja, K.; Alshamrani, S.S. Patron–Prophet Artificial Bee Colony Approach for Solving Numerical Continuous Optimization Problems. Axioms 2022, 11, 523.

Thirugnanasambandam, K., Rajeswari, M., Bhattacharyya, D. et al. Directed Artificial Bee Colony algorithm with revamped search strategy to solve global numerical optimization problems. Autom Softw Eng 29, 13 (2022).

Raghav, R. S., Thirugnanasambandam, K., Varadarajan, V., Vairavasundaram, S., & Ravi, L. (2022). Artificial Bee Colony Reinforced Extended Kalman Filter Localization Algorithm in Internet of Things with Big Data Blending Technique for Finding the Accurate Position of Reference Nodes. Big Data, 10(3), 186-203.




How to Cite

Budaraju, R. R. ., & Jammalamadaka, S. K. R. . (2024). Improvised Swarm Based Discrete Data Mining Approach for High Utility Item Sets. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 23–32. Retrieved from



Research Article