A fuzzy-graph-based approach to the determination of interestingness of association rules

'Interestingness' measures are used to rank rules according to the 'interest' a particular rule is expected to evoke in a user. In this paper, we introduce an aspect of interestingness called 'item-relatedness' to determine interestingness of item-pairs occurring in association rules. We elucidate and quantify three different types of item-relatedness. Relationships corresponding to item-relatedness proposed by us are shown to be captured by paths in a 'fuzzy taxonomy' (an extension of the concept hierarchy tree). We then combine these measures of item-relatedness to arrive at a total-relatedness measure. We finally demonstrate the efficacy of this total measure on a sample taxonomy.