A new way for the exploration of a dataset based on a social choice inspired approach

The exploration of a data set consists in grouping similar data. The classical statistical methods often fail when there is is no minimal assumption on the clusters. Our approach is based on the links between data, but the pairwise comparison between data and the importance of the links depend heavily on context where data lies. We propose to analyze a dataset through methods of the social choice theory where data plays both the role of a candidate and the role of a voter. The candidates are ranked by the voters and each voter gives a score to each candidate according to his ranking. We propose one specific election for each voter based on his preferences. The voters of these elections have weights computed according to their respective behaviors. In this approach, the conventional similarity indices between data are used to define the electoral behavior of each data.