Association rule mining through adaptive parameter control in particle swarm optimization

Indira, K.; Kanmani, S.

doi:10.1007/s00180-014-0533-y

Cited by 11 publications

(9 citation statements)

References 35 publications

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“…Problem solving in numerical data association analysis is generally performed using several approaches, including discretization, distribution and optimization. That the discretization is performed using partitioning and combining, clustering [11], [12] and fuzzy [8] methods, and the optimization approach is solved using the optimized association rule [13], differential evolution [14], GA [3], [7] and PSO [4], [15] as shown in Figure 1.…”

Section: Issn: 2088-8708mentioning

confidence: 99%

“…In 2014, Indira and Kanmani conducted research using a PSO approach; however, they attempted to improve results and analysis time using an adaptive parameter determination process to determine various parameters, such the constant and weight value in a velocity equation. They developed the Apriori algorithm using a PSO approach (APSO), and the results demonstrated that this approach was faster and better compared to using only an Apriori method [15]. In addition, the combination of PSO and GSA has been conducted for solving optimal reactive power dispatch problem in power system.…”

Section: Figure 1 Numeric Association Analysis Rule Miningmentioning

confidence: 99%

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Improved optimization of numerical association rule mining using hybrid particle swarm optimization and cauchy distribution

Tahyudin¹,

Nambo²

2019

IJECE

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<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p>Particle Swarm Optimization (PSO) has been applied to solve optimization problems in various fields, such as Association Rule Mining (ARM) of numerical problems. However, PSO often becomes trapped in local optima. Consequently, the results do not represent the overall optimum solutions. To address this limitation, this study aims to combine PSO with the Cauchy distribution (PARCD), which is expected to increase the global optimal value of the expanded search space. Furthermore, this study uses multiple objective functions, i.e., support, confidence, comprehensibility, interestingness and amplitude. In addition, the proposed method was evaluated using benchmark datasets, such as the Quake, Basket ball, Body fat, Pollution, and Bolt datasets. Evaluation results were compared to the results obtained by previous studies. The results indicate that the overall values of the objective functions obtained using the proposed PARCD approach are satisfactory.</p></div></div></div>

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Section: Issn: 2088-8708mentioning

confidence: 99%

Section: Figure 1 Numeric Association Analysis Rule Miningmentioning

confidence: 99%

Improved optimization of numerical association rule mining using hybrid particle swarm optimization and cauchy distribution

Tahyudin¹,

Nambo²

2019

IJECE

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“…Discretization is performed using partition and combination, clustering, and fuzzy logic routines [6], [7]. Then, the optimization is performed using optimized ARM [9], differential evolution (DE) [10], GA [4], [7], [11], and PSO [5], [7], [12]. These work can be seen in figure 1.…”

Section: Related Workmentioning

confidence: 99%

“…The PSO method was discovered by Kennedy, an animal psychologist, and Eberhart, an electrical engineer, in 1995. They observed the swarming behaviors in flocks of birds, schools of fishes, or swarms of bees, and even in human groups [12].…”

Section: Psomentioning

confidence: 99%

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The Rule Extraction of Numerical Association Rule Mining Using Hybrid Evolutionary Algorithm

Tahyudin

Nambo

2017

EECSI

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The topic of Particle Swarm Optimization (PSO) has recently gained popularity. Researchers has used it to solve difficulties related to job scheduling, evaluation of stock markets and association rule mining optimization. However, the PSO method often encounters the problem of getting trapped in the local optimum. Some researchers proposed a solution to over come that problem using combination of PSO and Cauchy distribution because this performance proved to reach the optimal rules. In this paper, we focus to adopt the combination for solving association rule mining (ARM) optimization problem in numerical dataset. Therefore, the aim of this research is to extract the rule of numerical ARM optimization problem for certain multi-objective functions such as support, confidence, and amplitude. This method is called PARCD. It means that PSO for numerical association rule mining problem with Cauchy Distribution. PARCD performed better results than other methods such as MOPAR, MODENAR, GAR, MOGAR and RPSOA.

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Association rules and decision rules

Mokkadem

Pelletier

Raimbault

2023

Statistical Analysis

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Determining association rules of significant interest is an essential task within data mining and statistical analysis. In this paper, we first precisely define the notion of association rule. For this, we introduce a general model, which includes the usual transaction model, and which allows many operations on the association rules. Then, we interpret association rules as statistical decision rules. This interpretation leads to four decisional measures, one of them being the usual confidence. Then, we give some strategies based on the use of these four decisional measures in order to select or to construct association rules with a given consequent. We finally present an experimental study to illustrate these strategies. This study is carried out in R language, with the R‐package we specifically built for association rules mining.

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Association rule mining through adaptive parameter control in particle swarm optimization

Cited by 11 publications

References 35 publications

Improved optimization of numerical association rule mining using hybrid particle swarm optimization and cauchy distribution

Improved optimization of numerical association rule mining using hybrid particle swarm optimization and cauchy distribution

The Rule Extraction of Numerical Association Rule Mining Using Hybrid Evolutionary Algorithm

Association rules and decision rules

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