Abstract
The research aims to address the challenge of High Utility Itemsets Mining by developing an efficient swarm-based optimization algorithm. The primary objective is to navigate the complex search space in scenarios with a substantial number of items and a large database, enhancing the identification of successful goods based on both quantity and profit considerations. The contributions of this work include the development of highly efficient algorithms and empirical evidence showcasing their practical relevance in real-world contexts. The significance of high utility itemset mining in data analysis is underscored, emphasizing its ability to extract meaningful insights by focusing both the quantity and profit aspects of items. The proposed swarm-based optimization model contributes to advancing the field, offering a promising approach for extracting valuable knowledge from diverse datasets while addressing challenges posed by sensitive or private information.
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Juyal, Y., Sharma, S., Sharma, H.D., Singh, P., Mishra, S., Dhyani, S. (2024). Designing an Enhanced Swarm-Based Optimization Algorithm for High Utility Itemsets Mining. In: Owoc, M.L., Varghese Sicily, F.E., Rajaram, K., Balasundaram, P. (eds) Computational Intelligence in Data Science. ICCIDS 2024. IFIP Advances in Information and Communication Technology, vol 718. Springer, Cham. https://doi.org/10.1007/978-3-031-69986-3_31
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DOI: https://doi.org/10.1007/978-3-031-69986-3_31
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