Nowadays, the majority of data on the Internet is held in an unstructured format, like websites and e-mails. The importance of analyzing these data has been growing day by day. Similar to data mining on structured data, text mining methods for handling unstructured data have also received increasing attention from the research community. The paper deals with the problem of Association Rule Text Mining. To solve the problem, the PSO-ARTM method was proposed, that consists of three steps: Text preprocessing, Association Rule Text Mining using population-based metaheuristics, and text postprocessing. The method was applied to a transaction database obtained from professional triathlon athletes' blogs and news posted on their websites. The obtained results reveal that the proposed method is suitable for Association Rule Text Mining and, therefore, offers a promising way for further development.

中文翻译：

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用于关联规则文本挖掘的基于种群的元启发式算法

如今，Internet 上的大部分数据都以非结构化格式保存，例如网站和电子邮件。分析这些数据的重要性与日俱增。与结构化数据的数据挖掘类似，处理非结构化数据的文本挖掘方法也越来越受到研究界的关注。本文涉及关联规则文本挖掘的问题。为了解决这个问题，提出了 PSO-ARTM 方法，它包括三个步骤：文本预处理、使用基于群体的元启发式的关联规则文本挖掘和文本后处理。该方法应用于从专业铁人三项运动员的博客和发布在他们网站上的新闻中获得的交易数据库。获得的结果表明，所提出的方法适用于关联规则文本挖掘，因此，