首页|Findings from Western Norway University of Applied Sciences Provides New Data about Artificial Intelligence (Tku-pso: an Efficient Particle Swarm Optimization Model for Top-k High-utility Itemset Mining)
Findings from Western Norway University of Applied Sciences Provides New Data about Artificial Intelligence (Tku-pso: an Efficient Particle Swarm Optimization Model for Top-k High-utility Itemset Mining)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Research findings on Machine Learning - Artificial Intelligence are discussed in a new report. According to news reporting originating in Bergen, Norway, by NewsRx journalists, research stated, “Top-k high-utility itemset mining (top-k HUIM) is a data mining procedure used to identify the most valuable patterns within transactional data. Although many algorithms are proposed for this purpose, they require substantial execution times when the search space is vast.” The news reporters obtained a quote from the research from the Western Norway University of Applied Sciences, “For this reason, several meta-heuristic models have been applied in similar utility mining prob- lems, particularly evolutionary computation (EC). These algorithms are beneficial as they can find optimal solutions without exploring the search space exhaustively. However, there are currently no evolutionary heuristics available for top-k HUIM. This paper addresses this issue by proposing an EC-based particle swarm optimization model for top-k HUIM, which we call TKU-PSO. In addition, we have developed sev- eral strategies to relieve the computational complexity throughout the algorithm. First, redundant and unnecessary candidate evaluations are avoided by utilizing explored solutions and estimating itemset utili- ties. Second, unpromising items are pruned during execution based on a thresholdraising concept we call minimum solution fitness. Finally, the traditional population initialization approach is revised to improve the model’s ability to find optimal solutions in huge search spaces. Our results show that TKU-PSO is faster than state-of-the-art competitors in all datasets tested. Most notably, existing algorithms could not complete certain experiments due to excessive runtimes, whereas our model discovered the correct solutions within seconds.”
BergenNorwayEuropeArtificial IntelligenceAlgorithmsEmerging TechnologiesMachine LearningParticle Swarm OptimizationWestern Norway University of Applied Sciences
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