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Item New GPU-based Swarm Intelligence Approach For Reducing Big Association Rules Space(CERIST, 2017-06-14) Djenouri, Youcef; Bendjoudi, Ahcène; Djenouri, Djamel; Belhadi, Asma; Nouali-Taboudjemat, NadiaThis paper deals with exploration and mining of association rules in big data, with the big challenge of increasing computation time. We propose a new approach based on meta-rules discovery that gives to the user the summary of the rules’ space through a meta-rules representation. This allows the user to decide about the rules to take and prune. We also adapt a pruning strategy of our previous work to keep only the representatives rules. As the meta-rules space is much larger than the rules space, two approaches are proposed for efficient exploitation. The first one uses a bees swarm optimization method in the meta-rules discovery process, which is extended using GPU-based parallel programming to form the second one. The sequential version has been first tested using medium rules set, and the results show clear improvement in terms of the number of returned meta-rules. The two versions have then been compared on large scale rules sets, and the results illustrate the acceleration on the summarization process by the parallel approach without reducing the quality of resulted meta-rules. Further experiments on Webdocs big data instances reveal that the proposed method of pruning rules by summarizing meta-rules considerably reduces the association rules space compared to state-of-the-art association rules mining-based approaches.Item GPU-based Bio-inspired Model for Solving Association Rules Mining Problem(CERIST, 2017-03-06) Djenouri, Youcef; Bendjoudi, Ahcène; Djenouri, Djamel; Commuzi, Marcoproblem with the purpose of extracting the correlations between items in sizeable data instances. According to the state of the art, the bio-inspired approaches proved their usefulness by finding high number of satisfied rules in a reasonable time when dealing with medium size instances. These approaches are unsuitable for large databases and especially for those existing on the web such as the Webdocs instance. Recently, the Graphics Processor Units (GPU) is considered as one of the most used parallel hardware to solve large scientific complex problems. In this paper, we propose a new GPU-based model of the bio-inspired approaches for solving association rules mining problem. Our model benefits from the massively GPU threaded by evaluating multiple rules in parallel on GPU. To validate the proposed model, the most used bio-inspired approaches (GA, PSO, and BSO) have been executed on GPU 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 the genetic algorithm outperforms PSO and BSO. Moreover, it outperforms the state-of-the-art GPU-based ARM approaches when dealing with the challenging Webdocs instance.Item Impact of Genetic Algorithms Operators on Association Rules Extraction(CERIST, 2016-10-02) Hamdad, Leila; Ournani, Zakaria; Benatchba, Karima; Bendjoudi, AhcèneIn this paper, we study the impact of GAs’ components such as encoding, different crossover, mutation and replacement strategies on the number of extracted association rules and their quality. Moreover, we propose a strategy to manage the population. The later is organized in classes where each one encloses same size rules. Each class can be seen as a population on which a GA is applied. All tests are conducted on two types of benchmarks : synthetic and real ones of different sizes.Item GPU parallel B&B for the Blocking job shop scheduling problem.(CERIST, 2016-02-22) Dabah, Adel; Bendjoudi, Ahcène; Ait Zai, Abdelhakim; El Baz, DidierBranch and bound algorithms (B&B) are well known techniques for solving optimally combinatorial optimization problems. Nevertheless, these algorithms remain inefficient when dealing with large instances. This paper deals with the blocking job shop scheduling (BJSS), which is a version of classical job shop scheduling with no intermediate buffer between machines. This problem is an NP-hard problem and its exact resolution using the sequential approach is impractical. We propose in this paper a GPU-based parallelization in which a two level scheme is used. The first level is a node-based parallelization in which the bounding process is faster because it is calculated in parallel using several threads organized in one GPU block. To fully occupy the GPU, we propose a second level of parallelization which is a generalization of the first level. Therefore, for each iteration several search tree nodes are evaluated on the GPU using several thread-blocks. The obtained results, using the well-known Taillard instances, confirm the efficiency of the proposed approach. Also, the results show that our approach is 65 times faster than an optimized sequential B&B version.Item Parallel BSO Algorithm for Association Rules Mining using Master/Workers Paradigm(CERIST, 2015-07-07) Djenouri, Youcef; Bendjoudi, Ahcène; Djenouri, DjamelThe extraction of association rules from large transactional databases is considered in the paper using cluster architecture parallel computing. Motivated by both the successful sequential BSO-ARM algorithm, and the strong matching between this algorithm and the structure of the cluster architectures, we present in this paper a new parallel ARM algorithm that we call MW-BSO-ARM for Master/Workers version of BSO-ARM. The goal is to deal with large databases by minimizing the communication and synchronization costs, which represent the main challenges that faces any cluster architecture. The experimental results are very promising and show clear improvement that reaches 300% for large instances. For examples, in big transactional database such as WebDocs, the proposed approach generates 107 satisfied rules in only 22 minutes, while a previous GPU-based approach cannot generate more than 103 satisfied rules into 10 hours. The results also reveal that MWBSO-ARM outperforms the PGARM cluster-based approach in terms of computation time.Item Data reordering for minimizing threads divergence in GPU-based evaluating association rules(CERIST, 2015-03-26) Djenouri, Youcef; Bendjoudi, Ahcène; Mehdi, Malika; Habbas, Zineb; Nouali-Taboudjemat, NadiaThis last decade, the success of Graphics Processor Units (GPUs) has led researchers to launch a lot of works on solving large complex problems by using these cheap and powerful architecture. Association Rules Mining (ARM) is one of these hard problems requiring a lot of computational resources. Due to the exponential increase of data bases size, existing algorithms for ARM problem become more and more inefficient. Thus, research has been focusing on parallelizing these algorithms. Recently, GPUs are starting to be used to this task. However, their major drawback is the threads divergence problem. To deal with this issue, we propose in this paper an intelligent strategy called Transactions- based Reordering "TR" allowing an efficient evaluation of association rules on GPU by minimizing threads divergence. This strategy is based on data base re-organization. To validate our proposition, theoretical and experimental studies have been carried out using well-known synthetic data sets. The results are very promising in terms of minimizing the number of threads divergence.Item Association rules mining using evolutionary algorithms (CERIST, 2014-10-16) Djenouri, Youcef; Bendjoudi, Ahcène; Nouali-Taboudjemat, NadiaThis paper deals with association rules mining using evolutionary algorithms. All previous bio-inspired based association rules mining approaches generate non admissible rules which the end-user can not exploit them. In this paper, we propose two approaches permit to avoid non admissible rules by developing new strategy called delete and decomposition strategy. If an item is appeared in the antecedent and the consequent parts of given rule, this rule is composed on two admissible rules. Then, we delete such item to the antecedent part of the first rule and we delete the same item to the consequent part of the second rule. We also proposed two approaches (IARMGA and IARMMA), the first approach uses a classical genetic algorithm in the search process. However, the second one employs a mimetic algorithm to improve the quality of returned rules. To demonstrate the suggested approaches, several experiments have been carried out using both synthetic and reals instances. The results reveal that it has a compromise between the execution time and the quality of output rules. Indeed, IARMGA is faster than IARMMA whereas the last one outperforms IARMGA in terms of rules quality.Item An Efficient Measure for Evaluating Association Rules(CERIST, 2014-06-24) Djenouri, Youcef; Gheraibai, Youcef; Mehdi, Malika; Bendjoudi, Ahcène; Nouali-Taboudjemat, NadiaAssociation rules mining (ARM) has attracted a lot of attention in the last decade. It aims to extract a set of relevant rules from a given database. In order to evaluate the quality of the resulting rules, existing measures, such as support and confidence, allow to evaluate the resulted rules of ARM process separately, missing the different dependencies between the rules. This paper addresses the problem of evaluating rules by taking into account two aspects : (1) The accuracy of the returned rules on the input data and (2) the distance between the returned rules. The rules set that covers the maximum of rules space is considered. To analyze the behavior of the proposed measure, it has been tested on two recent ARM algorithms BSO-ARM and HBSO-TS.Item Parallel Association Rules Mining Using GPUs and Bees Behaviors(CERIST, 2014-06-24) Djenouri, Youcef; Bendjoudi, Ahcène; Mehdi, Malika; Nouali-Taboudjemat, Nadia; Habbas, ZinebThis paper addresses the problem of association rules mining with large data sets using bees behaviors. The bees swarm optimization method have been successfully applied on small and medium data size. Nevertheless, when dealing Webdocs benchmark (the largest benchmark on the web), it is bluntly blocked after more than 15 days. Additionally, Graphic processor Units are massively threaded providing highly intensive computing and very usable by the optimization research community. The parallelization of such method on GPU architecture can be deal large data sets as the case of WebDocs in real time. In this paper, the evaluation process of the solutions is parallelized. Experimental results reveal that the suggested method outperforms the sequential version at the order of ×100 in most data sets, furthermore, the WebDocs benchmark is handled with less than ten hours.Item Parallel B&B Algorithm for Hybrid Multi-core/GPU Architectures(CERIST, 2013) Bendjoudi, Ahcène; Mehdi, MalikaB&B algorithms are well known techniques for exact solving of combinatorial optimization problems. They perform an implicit enumeration of the search space instead of exhaustive one. Based on a pruning technique, they reduce considerably the computation time required to explore the whole search space. Nevertheless, these algorithms remain inefficient when dealing with large combinatorial optimization instances. They are time-intensive and they require a huge computing power to be solved optimally. Nowadays, multi-core-based processors and GPU accelerators are often coupled together to achieve impressive performances. However, classical B&B algorithms must be rethought to deal with their two divergent architectures. In this paper, we propose a new B&B approach exploiting both the multi-core aspect of actual processors and GPU accelerators. The proposed approaches have been executed to solve FSP instances that are well-known combinatorial optimization benchmarks. 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 hybrid B&B approach speeds up the execution time up to x123 over the sequential mono-core B&B algorithm.