MaxMin clustering for historical analogy

Historical analogy is the ability to use historical knowledge to consider solutions for a present event, and it can be promoted by group learning. However, group creation for promoting the ability has been unexplored. This study proposes a novel clustering algorithm, named MaxMin clustering (MMC), to enhance discussions of group learning toward promoting historical analogy. The key concept is group formation by aggregating similar and different users. MMC uses aspects provided by users for the same present event. Subsequently, it solves maximum and minimum optimization problems to find similar and different users by counting the number of aspects shared by them. MMC is implemented and evaluated through comparison with other clustering algorithms; the comparison is based on the degree to which the generated clusters satisfy conditions for enhancing discussions of group learning toward promoting historical analogy. The experimental results prove that only MMC can generate suitable groups.