The group ranking problem is used to construct coherent aggregate results from preference data provided by decision makers. Although there have been different input formats used to represent user preferences, they share a common weakness, that the input mode is static. In other words, users must provide all the preference data at one time. To overcome this weakness, we propose a framework which allows users to provide partial and/or incomplete preference data at multiple times. Since this is a complicated issue, we specifically focus on a particular aspect as a first attempt at this framework. Accordingly, we reexamine a variant of the group ranking problem, the maximum consensus mining problem, which will give the longest ranking lists of alternatives that agree with the majority and disagree only with the minority, under the dynamic input mode assumption. An algorithm is developed to determine the maximum consensus sequences from the users' partial ranking data. Finally, extensive experiments are carried out using synthetic data sets. The results indicate that the proposed method is computationally efficient, and can effectively identify consensus among all users. (C) 2009 Elsevier B.V. All rights reserved.
