WIP: mining Weighted Interesting Patterns with a strong weight and/or support affinity
Unil Yun, John J. Leggett · 2006
In this paper, we present a new algorithm, Weighted Interesting Pattern mining (WIP) in which a new measure, weight-confidence, is developed to generate weighted hyperclique patterns with similar levels of weights. A weight range is used to decide weight boundaries and an h-confidence serves to identify strong support affinity patterns. WIP not only gives a balance between the two measures of weight and support, but also considers weight affinity and/or support affinity between items within patterns so more valuable patterns can be generated. A comprehensive performance study shows that WIP is efficient in weighted frequent pattern mining. Moreover, it generates fewer but more valuable patterns for users.