One of the most famous algorithms in the field of focused exploration of data mining correlation rules is the Apriori algorithm and its many developed versions. But what can be raised as a major challenge in this field is the proper application of this algorithm in the distributed environments of today's world. In this research, a parallelization-based approach is proposed to improve the performance of the Apriori algorithm in the process of exploring recurring patterns on network topologies. The proposed approach includes two major features: (1) combining the node centrality criterion and the Apriori algorithm to identify frequent patterns, (2) using the mapping/reduction method in order to create parallel processing and achieve optimal values in the shortest time. Also, this approach pursues three main goals: reducing the temporal and spatial complexity of the Apriori algorithm, improving the process of extracting dependency rules and identifying recurring patterns, comparing the performance of the proposed approach on different network topologies in order to determine the advantages and disadvantages of each topology. To prove the superiority of the proposed method, a comparison has been made between our approach and the basic Apriori algorithm. The evaluation results of the methods prove that the proposed approach provides an acceptable performance in terms of execution time criteria compared to other methods. پرونده مقاله

Core vector regression (CVR) is an extension of the core vector machine algorithm for regression estimation by generalizing the minimum bounding ball (MEB) problem. As an estimator, both the kernel function and its parameters can significantly affect the prediction precision of CVR. In this paper, a method to improve CVR performance using pre-processing based on data feature extraction and Grid algorithm is proposed to obtain appropriate parameters values of the main formulation and its kernel function. The CVR estimated mean absolute error rate here is the evaluation criterion of the proposed method that should be minimized. In addition, some benchmark datasets out of different databases were used to evaluate the proposed parameter optimization approach. The obtained numerical results show that the proposed method can reduce the CVR error with an acceptable time and space complexity. Therefore, it is able to deal with very large data and real world regression problems. پرونده مقاله