International Journal of Computer Applications
The mining of rare item sets involves finding rarely occurring items. It is difficult to mining rare item sets with a single minimum support (min-sup) constraint because low min-sup can result in generating too many rules in which some of them can be uninteresting. In the paper, "Multiple min-sup frameworks" was proposed to efficiently discover rare item sets. However, that model still extracts uninteresting rules if the items' frequencies in a dataset vary widely. In this paper, the authors are using the notion of "Item-to-pattern difference" and multiple min-sup based FP-growth like approach proposed in to efficiently discover rare item sets in the distributed environment.