Abstract
Recently, the discovery of association rules and the consequent mining frequent patterns have attracted the attention of many researchers to discover unknown relationships in big data, especially in networking and distributed environments. In this research, a parallelization-based approach is proposed to improve the performance of the Apriori algorithm in repetitive mining patterns on network topologies. The proposed approach includes two main features: (1) combining centrality criteria of the node and the Apriori algorithm to identify repetitive patterns and (2) using the mapping/reduction method to create parallel processing and achieve optimal values in the shortest time. This approach also pursues three main objectives: reducing the temporal and spatial complexity of the Apriori algorithm, improving the association rules mining process and identifying repetitive patterns, and comparing the proposed approach’s performance on different network topologies to determine the advantages and disadvantages of each topology. Comparing our proposed method and the basic Apriori algorithm, it is concluded that our approach provides acceptable efficiency in terms of evaluation criteria such as energy consumption, network lifetime, and runtime compared to other methods. Experimental results also show that when using our proposed method compared to the basic Apriori algorithm, network life is increased by 7.1%, the runtime is reduced by 43.2%, and the energy consumption is saved by about 41.2%.
Highlights
Data mining is a set of techniques that allows a person to move beyond ordinary data processing to the mining analysis of massive data and the mining of valuable information contained in them [1, 2]
A review of previous studies shows that exploring frequent patterns is the most important and Wireless Communications and Mobile Computing central part of the process of discovering association rules, which has been widely used in network topologies to discover unknown relationships [7]
Zoraghchian and Sohrabi [17] proposed using the butterfly optimization algorithm (BOA) in the process of mining the association rules to increase the efficiency of the basic algorithms
Summary
Data mining is a set of techniques that allows a person to move beyond ordinary data processing to the mining analysis of massive data and the mining of valuable information contained in them [1, 2]. E most important reason that made data mining the focus of attention in the information industry was the availability of large volumes of data and the urgent need to extract useful information and knowledge from this massive volume of data [4,5,6,7] Reviewing articles in this field shows that the essential part of this process is discovering repetitive items and patterns, which seems quite logical given the time-consuming nature of this part of the process [8]. (i) Reducing the temporal and spatial complexities of the Apriori algorithm (ii) Improving the efficiency of the association rules mining process and find repetitive patterns (iii) Comparing the performance of the proposed approach on different network topologies with existing methods
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