In this paper we present an approa ch to mitigate the `rule immensity' and the resulting `understandability' problem in association rule (AR) mining. Clustering `similar' rules facilitates exploration of connections among rules and discovery of the underlying structure. This in turn promotes the proactive discovery of chance events in a retail market. We first introduce the notion of `tightness' of an AR. It reveals the strength of binding between various items present in an AR. We elaborate on its usefulness in the retail market-basket context. After providing the intuition, we develop a distance -function on the basis of `tightness.' This distance function forms the basis for clustering ARs. Average linkage method is used to cluster ARs obtained from a small artificial dataset. Clusters thus obtained are compared with those obtained by running a standard method (from recent data mining literature) on the same data set.