A Survey on Particle Swarm Optimization for Association Rule Mining

Li, Guangquan; Wang, Ting; Chen, Qi; Shao, Peng; Xiong, Naixue; Vasilakos, Athanasios V.

doi:10.3390/electronics11193044

Cited by 9 publications

(5 citation statements)

References 107 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, there has been a growing interest in applying heuristic-based techniques to frequent itemset mining [40,41]. In these algorithms, approaches such as Genetic Algorithm [42,43,44] or Particle Swarm Optimization [45,46,47] are employed to generate a population of candidate itemsets, utilizing the support of these itemsets as the fitness value. During each iteration of the algorithm, the population is updated by introducing new items into the itemsets.…”

Section: Related Workmentioning

confidence: 99%

Probabilistic Support Prediction: Fast Frequent Itemset Mining in Dense Data

Sadeequllah,

Rauf,

Rehman

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Frequent itemset mining (FIM) is a highly resource-demanding data-mining task fundamental to numerous data-mining applications. Support calculation is a frequently performed computation-intensive operation of FIM algorithms, whereas storing transactional data is memory-intensive. The FIM is even more resource-hungry for dense data than for sparse data. The rapidly growing size of datasets further exacerbates this situation and necessitates the design of out-of-the-box highly efficient solutions. This paper proposes a novel approach to frequent itemset mining for dense datasets. This approach, after the initial stage, does not use transactional data, which makes it memory efficient. It also replaces processing-intensive support calculations with efficient support predictions, which are probabilistic and need no transactional data. To predict the support of an itemset, it only needs the support of its subsets. However, this technique works only for itemsets of size three or higher. We also propose an FIM algorithm ProbBF, which incorporates this technique. The ProbBF discards transactional data after it uses it to calculate frequent one and two-size itemsets. For the itemsets of size 𝑘, where 𝑘 ≥ 3, ProbBF uses the proposed probabilistic technique to predict their support. It is considered frequent if the predicted support is greater than a given threshold. Our experiments show that ProbBF is efficient in both time and space against state-of-the-art FIM algorithms that use transactional data. The experiments also show that ProbBF can successfully generate the majority of the frequent itemsets on real-world datasets. Since ProbBF is probabilistic, some loss in quality is inevitable.

show abstract

Section: Related Workmentioning

confidence: 99%

Probabilistic Support Prediction: Fast Frequent Itemset Mining in Dense Data

Sadeequllah,

Rauf,

Rehman

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Based on the standardized values of the indicators, the judgment matrix t R is obtained. where the columns of t R represent the standardized values of n specific indicators of m enterprises, and the judgment matrix is the basis for determining the weights of the indicators using the particle swarm algorithm [17]. The judgment matrix for the evaluation of each data of the enterprise is shown below:…”

Section: Weight Determination Model Based On Particle Swarm Algorithmmentioning

confidence: 99%

Based on the integration of multimedia e-commerce environment in the enterprise management innovation technology applications

2023

Applied Mathematics and Nonlinear Sciences

View full text Add to dashboard Cite

This paper first applies Multi-Agents’ distributed data management model to establish an enterprise economic data mining platform, combined with a clustering algorithm to extract valuable economic data from a multi-source heterogeneous enterprise database. The weights of indicators are solved by particle cluster algorithm, and the linkage vector distance and set-pair analysis of linkage degree are introduced to construct the innovative technology of enterprise economics and management under the integrated multimedia e-commerce environment. The study shows that the enterprise macro optimal economic index weight of this paper’s method is 0.812, and the production qualification rate of the enterprise is as high as 98.8%, which indicates that this paper’s method can meet the innovation of the enterprise development, and realize the enterprise business management and development strategy is of great significance.

show abstract

“…This has been demonstrated in hybrids that involve PSO with genetic algorithms (GAs), ant colony optimization, and differential evolution. Each of these hybrids showed superior performance compared to the standard PSO, finding wide applications in fields such as engineering, finance, and image processing [25,26]. Specific strategies have been adopted to enhance the social and cooperative aspects of PSO.…”

Section: Introductionmentioning

confidence: 99%

“…In a similar vein, an adaptive simulated annealing-parallel particle swarm optimization (ASA-PPSO) approach was developed, incorporating PSO with an infix condition that applies simulated annealing (SA). Moreover, hybridizing differential evolution (DE) with PSO has been explored in various ways, one of which involves using DE to maneuver particles and thereby enhance the convergence rate of PSO [26,34].…”

Section: Introductionmentioning

confidence: 99%

Identification of Mechanical Parameters in Flexible Drive Systems Using Hybrid Particle Swarm Optimization Based on the Quasi-Newton Method

Hafez,

Dhaouadi

2023

Algorithms

View full text Add to dashboard Cite

This study presents hybrid particle swarm optimization with quasi-Newton (HPSO-QN), a hybrid optimization method for accurately identifying mechanical parameters in two-mass model (2MM) systems. These systems are commonly used to model and control high-performance electric drive systems with elastic joints, which are prevalent in modern industrial production. The proposed method combines the global exploration capabilities of particle swarm optimization (PSO) with the local exploitation abilities of the quasi-Newton (QN) method to precisely estimate the motor and load inertias, shaft stiffness, and friction coefficients of the 2MM system. By integrating these two optimization techniques, the HPSO-QN method exhibits superior accuracy and performance compared to standard PSO algorithms. Experimental validation using a 2MM system demonstrates the effectiveness of the proposed method in accurately identifying and improving the mechanical parameters of these complex systems. The HPSO-QN method offers significant implications for enhancing the modeling, performance, and stability of 2MM systems and can be extended to other systems with flexible shafts and couplings. This study contributes to the development of accurate and effective parameter identification methods for complex systems, emphasizing the crucial role of precise parameter estimation in achieving optimal control performance and stability.

show abstract

A Survey on Particle Swarm Optimization for Association Rule Mining

Cited by 9 publications

References 107 publications

Probabilistic Support Prediction: Fast Frequent Itemset Mining in Dense Data

Probabilistic Support Prediction: Fast Frequent Itemset Mining in Dense Data

Based on the integration of multimedia e-commerce environment in the enterprise management innovation technology applications

Identification of Mechanical Parameters in Flexible Drive Systems Using Hybrid Particle Swarm Optimization Based on the Quasi-Newton Method

Contact Info

Product

Resources

About