Population-based metaheuristics for Association Rule Text Mining
This addresses text mining for unstructured data like websites and emails, but it is incremental as it adapts existing metaheuristics to a specific domain.
The paper tackled Association Rule Text Mining on unstructured text data by proposing the PSO-ARTM method, which applied population-based metaheuristics to a transaction database from triathlon athletes' blogs and news, showing the method is suitable for this task.
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.