  The problem of frequent pattern mining has been studied quite extensively for
various types of data, including sets, sequences, and graphs. Somewhat
surprisingly, another important type of data, namely rank data, has received
very little attention in data mining so far. In this paper, we therefore
addresses the problem of mining rank data, that is, data in the form of
rankings (total orders) of an underlying set of items. More specifically, two
types of patterns are considered, namely frequent rankings and dependencies
between such rankings in the form of association rules. Algorithms for mining
frequent rankings and frequent closed rankings are proposed and tested
experimentally, using both synthetic and real data.
