TY - JOUR A2 - Ding, Shifei AU - Xu, Yang AU - Zeng, Mingming AU - Liu, Quanhui AU - Wang, Xiaofeng PY - 2014 DA - 2014/08/26 TI - A Genetic Algorithm Based Multilevel Association Rules Mining for Big Datasets SP - 867149 VL - 2014 AB - Multilevel association rules mining is an important domain to discover interesting relations between data elements with multiple levels abstractions. Most of the existing algorithms toward this issue are based on exhausting search methods such as Apriori, and FP-growth. However, when they are applied in the big data applications, those methods will suffer for extreme computational cost in searching association rules. To expedite multilevel association rules searching and avoid the excessive computation, in this paper, we proposed a novel genetic-based method with three key innovations. First, we use the category tree to describe the multilevel application data sets as the domain knowledge. Then, we put forward a special tree encoding schema based on the category tree to build the heuristic multilevel association mining algorithm. As the last part of our design, we proposed the genetic algorithm based on the tree encoding schema that will greatly reduce the association rule search space. The method is especially useful in mining multilevel association rules in big data related applications. We test the proposed method with some big datasets, and the experimental results demonstrate the effectiveness and efficiency of the proposed method in processing big data. Moreover, our results also manifest that the algorithm is fast convergent with a limited termination threshold. SN - 1024-123X UR - https://doi.org/10.1155/2014/867149 DO - 10.1155/2014/867149 JF - Mathematical Problems in Engineering PB - Hindawi Publishing Corporation KW - ER -