  Rankings, representing preferences over a set of candidates, are widely used
in many information systems, e.g., group decision making and information
retrieval. It is of great importance to evaluate the consensus of the obtained
rankings from multiple agents. An overall measure of the consensus degree
provides an insight into the ranking data. Moreover, it could provide a
quantitative indicator for consensus comparison between groups and further
improvement of a ranking system. Existing studies are insufficient in assessing
the overall consensus of a ranking set. They did not provide an evaluation of
the consensus degree of preference patterns in most rankings. In this paper, a
novel consensus quantifying approach, without the need for any correlation or
distance functions as in existing studies of consensus, is proposed based on a
concept of q-support patterns of rankings. The q-support patterns represent the
commonality embedded in a set of rankings. A method for detecting outliers in a
set of rankings is naturally derived from the proposed consensus quantifying
approach. Experimental studies are conducted to demonstrate the effectiveness
of the proposed approach.
