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Generating of derivative membership functions for fuzzy association rule mining by Particle Swarm Optimization

机译：通过粒子群算法生成模糊关联规则挖掘的导数隶属函数

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The association Rule Mining (ARM) is a data mining task that extracts relations between items based on the item's frequency. The ARM regards items with high frequency as more interesting than items with low frequency. In quantitative datasets, each item will be grouped into a large range of values. Therefore, items with low frequencies may not be considered as interesting. Hence, the possibility of extracting potentially interesting relations between these items will decrease. Thus, this deficiency brings a challenging issue to this field. Most of the existing methods for quantitative ARM in handling this problem are based on the Sharp Boundary Discretization methods and Clustering methods. These methods group each item into intervals with crisp boundaries which do not overlap. They bring some problems as well, such as ignoring or emphasizing more on values near the boundary of intervals. To deal with the problem of quantitative ARM, the combination of S and Z fuzzy shapes, which is combined with the Particle Swarm Optimization (PSO) is proposed in this paper to generate appropriate membership functions for each item. Fuzzy logic will group items into overlapping intervals and then, the fuzzy rules will be generated from the interesting items. The performances of the proposed methods are evaluated over Bilkent datasets and then, are compared with the results of clustering method (Fuzzy C-Means) in aspect of their capability to transform data to fuzzy data and then their efficiency are evaluated based on the quality of their generated rules. The results show the efficiency of the proposed method to extract the rules with more quality.
机译：关联规则挖掘（ARM）是一项数据挖掘任务，它根据项目的频率提取项目之间的关系。 ARM认为高频物品比低频物品更有趣。在定量数据集中，每个项目将被分组为较大范围的值。因此，低频物品可能不会被认为是有趣的。因此，提取这些项目之间潜在的有趣关系的可能性将降低。因此，这种缺陷给该领域带来了挑战性的问题。用于解决此问题的定量ARM的大多数现有方法都是基于Sharp Boundary Discretization方法和Clustering方法。这些方法将每个项目分组为具有不重叠的清晰边界的间隔。它们也带来一些问题，例如忽略或强调在区间边界附近的值。为了解决定量ARM问题，提出了S和Z模糊形状的组合，并结合粒子群优化（PSO）来为每个项目生成合适的隶属函数。模糊逻辑会将项目分组为重叠的间隔，然后，将根据感兴趣的项目生成模糊规则。在Bilkent数据集上评估所提出方法的性能，然后在将数据转换为模糊数据的能力方面与聚类方法（Fuzzy C-Means）的结果进行比较，然后根据质量的高低来评估其效率。他们生成的规则。结果表明，该方法具有更高的提取效率的有效性。

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