ItemSB: Itemsets with Statistically Distinctive Backgrounds Discovered by Evolutionary Method
ItemSB: Itemsets with Statistically Distinctive Backgrounds Discovered by Evolutionary Method
复制标题
ItemSB：通过进化方法发现具有统计上独特背景的项目集
DOI：
10.1142/s1793351x22420028
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发表时间：
2022
影响因子：
0.8
通讯作者：
Matsuno Shogo
中科院分区：
文献类型：
--
作者：
Shimada Kaoru;Arahira Takaaki;Matsuno Shogo
在本文中，我们提出了一种方法，发现组合的属性（即项目集）的统计特征的背景下，不获得频繁项目集。该方法考虑了具有众多属性的数据库，并且即使从不完整的数据库中，也可以直接从两个连续的感兴趣变量中的小群体中找到高度相关的属性的组合。由于所提出的方法确定了大规模数据中的局部模式，因此可以用作大规模数据分析的基础。进化计算的特点是网络结构和策略池的解决方案，在整个世代使用。此外，关联规则被用来概括的分析方法，统计上不同的背景（ItemSB）的项目集。用于分类的类关联规则构成了一种属性组合发现方法，当得到类属性的比率时，属性组合是有特征的。所提出的方法是统计双变量分析的扩展。此外，我们确定在满足相同条件的情况下，两个数据子组之间存在统计学差异的对比ItemSB。实验结果表明了该方法的特点和有效性。
In this paper, we propose a method for discovering combinations of attributes (i.e. itemsets) against a background of statistical characteristics without obtaining frequent itemsets. The method considers a database with numerous attributes and can directly find a combination of highly correlated attributes from small populations in two consecutive variables of interest even from an incomplete database. As the proposed method determines local patterns in large-scale data, it may be used as a basis for large-scale data analysis. Evolutionary computations characterized by a network structure and a strategy to pool solutions are used throughout generations. Moreover, association rules are used to generalize the analysis method as itemsets with statistically distinctive backgrounds (ItemSBs). The class-association rules used for classification constitute a discovery method of attribute combinations, which are characteristic when the ratio of class attributes is obtained. The proposed method is an extension to statistical bivariate analysis. In addition, we determine contrast ItemSBs that are statistically different between two subgroups of data while satisfying the same conditions. Experimental results show the characteristics and effectiveness of the proposed method.