Association rules have become an important paradigm in knowledge discovery. Nevertheless, the huge number of rules which are usually obtained from standard datasets limits their applicability. In order to solve this problem, several solutions have been proposed, as the definition of subjective measures of interest for the rules or the use of more restrictive accuracy measures. Other approaches try to obtain different kinds of knowledge, referred to as peculiarities, infrequent rules, or exceptions. In general, the latter approaches are able to reduce the number of rules derived from the input dataset. This paper is focused on this topic. We introduce a new kind of rules, namely, anomalous rules, which can be viewed as association rules hidden by a dominant rule. We also develop an efficient algorithm to find all the anomalous rules existing in a database.