Zineb Habbas

Bees swarm optimisation using multiple strategies for association rule mining

Association rules mining has been largely studied by the data mining community. ARM aims to extract the interesting rules from any given transactional database. This problem is well known to be time consuming in general. This paper deals with association rules mining algorithms to cope with very large databases and especially for those existing on the web. Many polynomial exact algorithms already proposed in literature have shown their efficiency when dealing with small and medium datasets. Unfortunately, their efficiency is not enough for handling the huge amount of data in the web context requiring a real time response. Not surprisingly, some bio-inspired methods seem to be clearly more appropriate. This paper mainly proposes a new ARM algorithm based on an improved version of bees swarm optimisation with three different heuristics for exploring the search area. This approach has been implemented and experimented on different dataset benchmarks with small size, medium size and large size. These first empirical results highlighted that our approach outperforms some other existing algorithms both in terms of fitness criterion and CPU time.

Bees Swarm Optimization for Web Association Rule Mining

This paper deals with Association Rules Mining algorithms for very large databases and especially for those existing on the web. The numerous polynomial exact algorithms already proposed in literature treated somehow in an efficient way data sets with reasonable size. However they are not capable to cope with a huge amount of data in the web context where the respond time must be very short. This paper, mainly proposes two new Association Rules Mining algorithms based on Genetic metaheuristic and Bees Swarm Optimization respectively. Experimental results show that concerning both the fitness criterion and the CPU time, IARMGA algorithm improved AGA and ARMGA two other versions based on genetic algorithm already proposed in the literature. Moreover, the same experience shows that concerning the fitness criterion, BSO-ARM achieved slightly better than all the genetic approaches. On the other hand, BSO-ARM is more time consuming. In all cases, we observed that the developed approaches yield useful association rules in a short time when comparing them with previous works.

GPU-based bees swarm optimization for association rules mining

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 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

Parallel BSO Algorithm for Association Rules Mining Using Master/Worker Paradigm

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Organizing association rules with meta-rules using knowledge clustering

× 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.

Bees Swarm Optimization Metaheuristic Guided by Decomposition for Solving MAX-SAT

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. Copyright

An Improved Evolutionary Approach for Association Rules Mining

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/worker 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 \(10^{7}\) satisfied rules in only 22 min, while a previous GPU-based approach cannot generate more than \(10^{3}\) satisfied rules into 10 h. The results also reveal that MW-BSO-ARM outperforms the PGARM cluster-based approach in terms of computation time.

A Forward-Checking algorithm based on a Generalised Hypertree Decomposition for solving non-binary constraint satisfaction problems

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.