Mining High Utility Itemset with Hybrid Ant Colony Optimization Algorithm

A significant area of study within data mining is high-utility itemset mining (HUIM). The exponential problem of broad search space usually comes up while using traditional HUIM algorithms when the database size or the number of unique objects is huge. Evolutionary computation (EC) -based algorithms have been presented as an alternate and efficient method to address HUIM problems since they can quickly produce a set of approximately optimum solutions. In transactional databases, finding entire high-utility itemset (HUIs) still need a lot of time using EC-based methods. In order to deal with this issue, we propose a hybrid Ant colony optimization-based HUIM algorithm. Genetic operators’ crossover is applied to the generated solution by the ant in the Ant Colony optimization algorithm. In this study, a single-point crossover is employed wherein, the crossover point is selected randomly and a mutation operator is applied by changing one or many random bits in a string. This technique requires less time to mine the same number of HUIs than state-of-the-art EC-based HUIM algorithms.

@article{Mohan2024,
  title     = {Mining High Utility Itemset with Hybrid Ant Colony Optimization Algorithm},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {12},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Keerthi Mohan and Anitha J},
  doi       = {10.14569/IJACSA.2024.0151264},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151264}
}