Bees swarm optimisation using multiple strategies for association rule mining

Djenouri, Youcef; Drias, Habiba; Habbas, Zineb

doi:10.1504/ijbic.2014.064990

Cited by 65 publications

(41 citation statements)

References 19 publications

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“…To deal with a huge transactional database in a reasonable time, many metaheuristics have already been applied to ARM. Some of these methods are based on evolutionary algorithms [31,42,48], and other ones on swarm intelligence [11,20,23,34].…”

Section: Introductionmentioning

confidence: 99%

GPU-based bees swarm optimization for association rules mining

et al. 2015

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Association rules mining (ARM) is a well-known combinatorial optimization problem aiming at extracting relevant rules from given large-scale datasets. According to the state of the art, the bio-inspired methods proved their efficiency by generating acceptable solutions in a reasonable time when dealing with small and medium size instances. Unfortunately, to cope with large instances such as the webdocs benchmark, these methods require more and more powerful processors and are time expensive. Nowadays, computing power is no longer a real issue. It can be provided by the power of emerging technologies such as graphics processing units (GPUs) that are massively multi-threaded processors. In this paper, we investigate the use of GPUs to speed up the computation. We propose two GPU-based bees swarm algorithms for association rules mining (single evaluation in GPU, SE-GPU and multiple evaluation in GPU, ME-GPU). SE-GPU aims at evaluating one rule at a time where each thread is associated with one transaction, whereas ME-GPU evaluates multiple rules in parallel on GPU where each thread is associated with several transactions. To validate our 123 Y. Djenouri et al. approaches, the two algorithms have been executed to solve well-known large ARM instances. Real experiments have been carried out on an Intel Xeon 64 bit quad-core processor E5520 coupled to an Nvidia Tesla C2075 GPU device. The results show that our approaches improve the execution time up to 100× over the sequential mono-core bees swarm optimization-ARM algorithm. Moreover, the proposed approaches have been compared with CPU multi-core ones (1-8 cores). The results show that they are faster than the multi-core versions whatever is the number of used cores.

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Section: Introductionmentioning

confidence: 99%

GPU-based bees swarm optimization for association rules mining

et al. 2015

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“…The results of this approach show that BSO-ARM performs better than all genetic algorithms. As extension to their work, the authors present an amelioration to BSO-ARM in [11], where three strategies to determine the search area of each bee are proposed (modulo, next, syntactic). These improvements yield quality to the rules extracted by BSO-ARM, but unfortunately, the algorithm takes more CPU time.…”

Section: Related Work On Armmentioning

confidence: 99%

“…Furthermore, traditional algorithms developed to solve ARM issue, such as Apriori [1,2], FPgrowth [21], needs a huge processing time to handle big databases. To overcome this drawback and deal with huge databases within reasonable time, studies use intelligent heuristic algorithms, which are already used to solve many NP-Complete problems, to discover association rules such as genetic algorithm [13,20], particle swarm optimization [29,37], Bee swarm algorithm [11] and recently, Bat algorithm [22,23]. Generally, Intelligent algorithms make individuals moving in an n-dimensional space to search the optimum solutions for an n-variable function optimization problem.…”

Section: Introductionmentioning

confidence: 99%

“…• Bees swarm optimization algorithm for ARM, i.e., BSO-ARM[11] • Hybrid Bees swarm optimization with tabu-search algorithm for ARM, i.e., HBSO-TS[10] • Ant colony optimization based Algorithm for ARM, i.e.,ACO R[34] • Grammar guided genetic programming for ARM, i.e., G3PARM[33] • Binary and continues gravitational search algorithm for ARM, i.e., ARMBGSA[28] …”

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Multi-swarm bat algorithm for association rule mining using multiple cooperative strategies

Heraguemi

Kamel

Drias³

2016

Appl Intell

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Association Rule Mining (ARM) can be considered as a combinatorial problem with the purpose of extracting the correlations between items in sizeable datasets. The numerous polynomial exact algorithms already proposed for ARM are unadapted for large databases and especially for those existing on the web. Assuming that datasets are a large space search, intelligent algorithms was used to found high quality rules and solve ARM issue. This paper deals with a cooperative multi-swarm bat algorithm for association rule mining. It is based on the bat-inspired algorithm adapted to rule discovering problem (BAT-ARM). This latter suffers from absence of communication between bats in the population which lessen the exploration of search space. However, it has a powerful rule generation process which leads to perfect local search. Therefore, to maintain a good trade-off between diversification and intensification, in our proposed approach, we introduce cooperative strategies between the swarms that already proved their efficiency in multi-swarm optimization algorithm(Ring, Master-slave). Furthermore, we innovate a new topology called Hybrid that merges Ring strategy with Master-slave plan previously developed in our earlier work [23]. A series of experiments are carried out on nine well known datasets in ARM field and the performance of proposed approach are evaluated and compared with those of other recently published methods. The results show a clear superiority of our proposal against its similar approaches in terms of time and rule quality. The analysis also shows a competitive outcomes in terms of quality in-face-of multi-objective optimization methods.

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“…To reduce the computation time of such algorithms, a second category, meta-heuristics based approaches, have been proposed. Examples of such approaches include ARMGA [10] and G3PARM [11] for evolutionary algorithms, PSO-ARM [12], ACO R [13] and BSO-ARM [14] for swarm intelligence. Authors in [15] have presented a survey on swarm intelligence (SI) approaches used for automatic programming.…”

Section: Introductionmentioning

confidence: 99%

Penguins Search Optimisation Algorithm for Association Rules Mining

Gheraibia¹

2016

CIT. J. comput. inf. technol.

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Association Rules Mining (ARM) is one of the most popular and well-known approaches for the decisionmaking process. All classic exhaustive ARM algorithms are time consuming and generate a very large number of association rules, even the recent proposed meta-heuristics based methods generate a small number of high quality rules but with high overlapping. To deal with this issue, we propose a new ARM approach based on penguins search optimisation algorithm (Pe-ARM for short). Moreover, an efficient measure is incorporated into the main process to evaluate the amount of overlapping among the generated rules. The proposed approach also ensures a good diversification over the whole solutions space. To demonstrate the effectiveness of the proposed approach, several experiments have been carried out on different data sets and specifically on the biological ones. The results reveal that the proposed approach outperforms the well known meta-heuristics ARM algorithms in both execution time and solution quality.

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Bees swarm optimisation using multiple strategies for association rule mining

Cited by 65 publications

References 19 publications

GPU-based bees swarm optimization for association rules mining

GPU-based bees swarm optimization for association rules mining

Multi-swarm bat algorithm for association rule mining using multiple cooperative strategies

Penguins Search Optimisation Algorithm for Association Rules Mining

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