Mining high utility itemsets using Shuffled Complex Evolution of particle swarm optimization (SCE-PSO) optimization algorithm

High-utility itemset mining is a recent research area in data mining. It attracts many researchers as it extracts profitable products from a database. It considers both the quantity and profit of products. Several algorithms have been presented to mine high-utility itemsets. There are some bio inspired algorithms like Genetic Algorithm, Particle swarm optimization and Ant colony system are also used to retrieve high utility itemsets. In this paper a Shuffled Complex Evolution of Particle Swarm Optimization (SCE-PSO) algorithm was used to retrieve high utility itemsets. In the SCE-PSO, a population of points is sampled randomly in the feasible space. Then the population is partitioned into several complexes, which is made to evolve based on PSO. At periodic stages in the evolution, the entire population is shuffled and points are reassigned to complexes. The performance of the SCE-PSO algorithm is compared to existing PSO and GA algorithms and results are compared. A benchmark dataset, Mushroom and Foodmart datasets are used for performance analysis. Execution times of the algorithms and the memory space used by the algorithms are considered as performance factors.