Impact of Genetic Algorithms Operators on Association Rules Extraction

Hamdad, Leila; Benatchba, Karima; Bendjoudi, Ahcène; Ournani, Zakaria

doi:10.1007/978-3-030-20518-8_62

Cited by 3 publications

(7 citation statements)

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“…9 PBI approach [20] for Association Rules Mining, Kuo et al [39]). It should be noted that, to our knowledge, the only comparison between the previous methods and APRIORI has been carried out in Hamdad et al [30] which show that GA-ARM have given the best results in terms of number of obtained rules when compared with AGA, IGA, ARMGA, MBAREA on three datasets: IBM Quest synthetic database, Mushroom and Chess [28]; UCI 2015). A recent survey have been proposed on heuristics in association rules mining (see [24]).…”

Section: Other Approximate Methodsmentioning

confidence: 99%

“…Since the GA are the most used metaheuristic in several ARM problems and these GAs differ in their construction according to the used encoding, crossover, etc., a questions arise; what is the impact of each component of GA on the AR and which components give the best AR? More recently, Hamdad et al [30] proposed to study the impact of all GA components (types of chromosomes encoding, types of crossover, replacement strategies, etc.) on the number and quality of returned AR.…”

Section: Genetic Algorithms (Gas) For Armmentioning

confidence: 99%

“…The impact of population size Fig. 7 Repartition of rules in classes, Hamdad et al [30] and number of iterations were also studied. The used GA called GA-ARM who gave the best results (association rules) after several tests on small and medium known dataset in this fields is the one that has as components: one rule encoding, where, a chromosome represents a rule as shown in Fig.…”

Section: Genetic Algorithms (Gas) For Armmentioning

confidence: 99%

“…In 2020, Hamdad et al [31] have proposed several strategies to parallelize the process of AR extraction using CPU and GPU. These parallelisms are performed on GA-ARM algorithm (4) presented in [30]. First, they proposed CPU parallel execution of GA-ARM to speed a process for extracting AR.…”

Section: Metaheuristics On Gpumentioning

confidence: 99%

“…19 Hybrid version: concurrent use of the GPU [31] To validate the proposed two parallel GAs, several tests were conducted to solve well-known large ARM instances [28]. The used GA is GA-ARM with the best parameters obtained in Hamdad et al [30]. Obtained results show that the parallel algorithms outperform state-of-the-art exact algorithms (APRIORI and FP-GROWTH) and approximate algorithms (SEGPU and ME-GPU) in terms of execution time.…”

Section: Metaheuristics On Gpumentioning

confidence: 99%

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Association Rules Mining

Hamdad

Benatchba

2021

SN COMPUT. SCI.

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Association rules mining (ARM) is an unsupervised learning task. It is used to generate significant and relevant association rules among items in a database. APRIORI and FP-GROWTH are the most popular and used algorithms nowadays for extracting such rules. They are exact methods that consist of two phases. First, frequent itemsets are generated. Then, the latter are used to generate rules. The main drawback of both algorithms is their high execution time. To overcome this drawback, metaheuristics have been proposed. Moreover, to optimize the execution time, since the amount of data is in continuous growth, some parallel architectures can be found in the literature. In this paper, we present an overview of existing literature that investigate the ARM problem using both metaheuristics and parallelism. We will focus on the recent algorithms that tackle these problems using approximate methods and GPU. We will present a non-exhaustive classification of different algorithms according to the type of execution (sequential or parallel) and type of method (exact or approximate).

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