Browsing by Author "Djenouri, Youcef"

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    An Efficient Measure for Evaluating Association Rules
    (CERIST, 2014-06-24) Djenouri, Youcef; Gheraibai, Youcef; Mehdi, Malika; Bendjoudi, Ahcène; Nouali-Taboudjemat, Nadia
    Association 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.
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    An Efficient Measure for Evaluating Association Rules
    (2014-08) Djenouri, Youcef; Gheraibai, Youcef; Mehdi, Malika; Bendjoudi, Ahcène
    Association 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.
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    Association rules mining using evolutionary algorithms
    (LNCS, 2014-10-16) Djenouri, Youcef; Bendjoudi, Ahcène; Nouali-Taboudjemat, Nadia
    This 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.
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    Association rules mining using evolutionary algorithms 
    (CERIST, 2014-10-16) Djenouri, Youcef; Bendjoudi, Ahcène; Nouali-Taboudjemat, Nadia
    This 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.
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    Data Mining-Based Decomposition for Solving the MAXSAT Problem: Toward a New Approach
    (IEEE, 2017-06) Djenouri, Youcef; Habbas, Zineb; Djenouri, Djamel
    A new approach decomposes a MAXSAT instance and then applies clustering via data mining decomposition techniques, with every cluster resulting from the decomposition separately solved to construct a partial solution. All partial solutions are then merged to build the global solution.
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    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, Nadia
    This 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.
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    Data reordering for minimizing threads divergence in GPU-based evaluating association rules
    (2015-06) Djenouri, Youcef; Bendjoudi, Ahcène; Mehdi, Malika; Habbas, Zineb; Nouali-Taboudjemat, Nadia
    This 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 datasets. The results are very promising in terms of minimizing the number of threads divergence.
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    GPU-based Bees Swarm Optimization for Association Rules Mining
    (Springer, 2014) Djenouri, Youcef; Bendjoudi, Ahcène; Mehdi, Malika; Nouali-Taboudjemat, Nadia; Habbas, Zineb
    Association Rules Mining (ARM) is a well-known combinatorial optimization problem aiming at extracting relevant rules from given large scale data sets. 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 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 (SE-GPU and 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 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 x100 over the sequential mono-core BSO-ARM algorithm. Moreover, the proposed approaches have been compared with CPU multi-core ones (1 to 8 cores). The results show that they are faster than the multi-core versions what ever the number of used cores.
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    GPU-based Bio-inspired Model for Solving Association Rules Mining Problem
    (CERIST, 2017-03-06) Djenouri, Youcef; Bendjoudi, Ahcène; Djenouri, Djamel; Commuzi, Marco
    problem 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.
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    Machine Learning for Smart Building Applications: Review and Taxonomy
    (ACM, 2019-03) Djenouri, Djamel; Laidi, Roufaida; Djenouri, Youcef; Balasingham, Ilangko
    The use of machine learning (ML) in smart building applications is reviewed in this paper. We split existing solutions into two main classes, occupant-centric vs. energy/devices centric. The first class groups solutions that use ML for aspects related to the occupants, including (1) occupancy estimation and identification, (2) activity recognition, and (3) estimating preferences and behavior. The second class groups solutions that use ML to estimate aspects related either to energy or devices. They are divided into three categories, (1) energy profiling and demand estimation, (2) appliances profiling and fault detection, and (3) inference on sensors. Solutions in each category are presented, discussed and compared, as well as open perspectives and research trends. Compared to related state-of-the-art survey papers, the contribution herein is to provide a comprehensive and holistic review from the ML perspectives rather than architectural and technical aspects of existing building management systems. This is by considering all types of ML tools, buildings, and several categories of applications, and by structuring the taxonomy accordingly. The paper ends with a summary discussion of the presented works, with focus on lessons learned, challenges, open and future directions of research in this field.
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    Machine Learning for Smart Building Applications: Review and Taxonomy
    (ACM, 2019-03) Djenouri, Djamel; Laidi, Roufaida; Djenouri, Youcef; Balasingham, Ilangko
    The use of machine learning (ML) in smart building applications is reviewed in this paper. We split existing solutions into two main classes, occupant-centric vs. energy/devices centric. The first class groups solutions that use ML for aspects related to the occupants, including (1) occupancy estimation and identification, (2) activity recognition, and (3) estimating preferences and behavior. The second class groups solutions that use ML to estimate aspects related either to energy or devices. They are divided into three categories, (1) energy profiling and demand estimation, (2) appliances profiling and fault detection, and (3) inference on sensors. Solutions in each category are presented, discussed and compared, as well as open perspectives and research trends. Compared to related state-of-the-art survey papers, the contribution herein is to provide a comprehensive and holistic review from the ML perspectives rather than architectural and technical aspects of existing building management systems. This is by considering all types of ML tools, buildings, and several categories of applications, and by structuring the taxonomy accordingly. The paper ends with a summary discussion of the presented works, with focus on lessons learned, challenges, open and future directions of research in this field.
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    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, Nadia
    This 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.
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    Parallel Association Rules Mining Using GPUs and Bees Behaviors
    (CERIST, 2014-06-24) Djenouri, Youcef; Bendjoudi, Ahcène; Mehdi, Malika; Nouali-Taboudjemat, Nadia; Habbas, Zineb
    This 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.
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    Parallel BSO Algorithm for Association Rules Mining Using Master/Worker Paradigm
    (2015-09-06) Djenouri, Youcef; Bendjoudi, Ahcène; Djenouri, Djamel; Habbas, Zineb
    The 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.
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    Parallel BSO Algorithm for Association Rules Mining using Master/Workers Paradigm
    (CERIST, 2015-07-07) Djenouri, Youcef; Bendjoudi, Ahcène; Djenouri, Djamel
    The 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.
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    Parallel Rules Mining Using GPUs and Bees Behaviors
    (2014-08) Djenouri, Youcef; Bendjoudi, Ahcène; Mehdi, Malika; Nouali-Taboudjemat, Nadia; Habbas, Zineb
    This 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.
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    Pruning Irrelevant Association Rules Using Knowledge Mining
    (2014) Djenouri, Youcef; Derias, Habiba; Bendjoudi, Ahcène
    The efficiency of existing association rules mining algorithms afford large number of delivered rules that the user can not exploit them easily. Consequently, thinking about another mining of these generated rules becomes essential task. For this, the present paper explores metarules extraction in order to prune the irrelevant rules. It first focuses on clustering association rules for large datasets. This allows the user better organising and interpreting the rules. To more go down in our mining, different dependencies between rules of the same cluster are extracted using meta-rules algorithm. Then, pruning algorithm uses these dependencies to delete the deductive rules and keep just the representative rules for each cluster. The proposed approach is tested on different experiments including clustering, meta-rules and pruning steps. The result is very promising in terms of the number of returned rules and their quality.
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    Reducing thread divergence in GPU-based bees swarm optimization applied to association rule mining
    (John Wiley & Sons, Ltd., 2016) Bendjoudi, Ahcène; Djenouri, Youcef; Habbas, Zineb; Mehdi, Malika; Djenouri, Djamel
    The association rules mining (ARM) problem is one of the most important problems in the area of data mining. It aims at finding all relevant association rules from transactional databases. It is CPU time intensive and requires a huge computing power when dealing with large transactional databases. To deal with this issue, Graphics Processing Units (GPUs) are a powerful tool to speed up the search process. However, their performance is closely subject to thread/branch divergence resulting from the single instruction multiple data parallel model of GPUs. In this paper, we propose three approaches based on database reorganization, aiming to reduce thread divergence in GPU-based bees swarm optimization metaheuristic for ARM, respectively, named block-based reordering, transactions-based reordering, and transactions-based reordering with median value. Theoretical and experimental studies have been carried out using well-known large ARM instances. The experiments have been performed on an Intel Xeon 64 bit quad-core processor E5520 coupled to Nvidia Tesla C2075 448 cores. The results show that the proposed approaches minimize considerably the number of thread divergence and improve the overall execution time. Indeed, the number of thread divergence occurrences has been reduced by up to eight times making the execution much faster.