Association rule mining is a powerful data analytic technique used for extracting information from transaction databases with a collection of itemsets. The aim is to indicate what item goes with what item (ie, an association rule) in a set of collected transactions. It is extensively used in text analytics of text records or social media. Here we use Compositional Data analysis (CoDa) techniques to generate new visualizations and insights from association rule mining. These CoDa methods show the relationship between itemsets, their strength, and direction of dependency. Moreover, after expressing each association rule as a contingency table, we discuss two statistical tests to guide identification of the relevant rules by analyzing the relative importance of the elements of the table. As an example, we use these visualizations and statistical tests for investigating the association of negative mood emotions to various types of headache/migraine events. Data for those analysis comes from N1-Headache^TM, a digital platform where individual users record attacks and symptoms as well as their daily exposure to a list of potential factors.

中文翻译：

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关联规则的测量、选择和可视化：组合数据视角

关联规则挖掘是一种强大的数据分析技术，用于从具有项集集合的事务数据库中提取信息。目的是在一组收集的交易中指示什么项目与什么项目（即关联规则）相匹配。它广泛用于文本记录或社交媒体的文本分析。在这里，我们使用组合数据分析 (CoDa) 技术从关联规则挖掘中生成新的可视化和洞察力。这些 CoDa 方法显示了项集之间的关系、它们的强度和依赖方向。此外，在将每个关联规则表示为列联表之后，我们讨论了两个统计检验，通过分析表中元素的相对重要性来指导相关规则的识别。举个例子，我们使用这些可视化和统计测试来调查负面情绪情绪与各种类型的头痛/偏头痛事件的关联。这些分析的数据来自 N1-Headache^TM，一个数字平台，个人用户可以在其中记录攻击和症状以及他们每天接触的潜在因素列表。