1 research outputs found
Population-based metaheuristics for Association Rule Text Mining
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