Discovering a bucket order B from a collection of possibly noisy full rankings is a fundamental problem that relates to various applications involving rankings. Informally, a bucket order is a total order that allows "ties" between items in a bucket. A bucket order B can be viewed as a"representative" that summarizes a given set of full rankings {T1, T2, . . . , Tm}, or conversely B can be an "approximation" of some "ground truth" G where the rankings {T1, T2, . . . , Tm} are the "linear extensions" of G. Current work of finding bucket orders such as the dynamic programming algorithm is mainly developed from the"representative" perspective, which maximizes items' intra-bucket similarity when forming a bucket. The underlying idea of maximizing intra-bucket similarity is realized via minimizing the sum of the deviations of median ranks within a bucket. In contrast, from the "approximation" perspective, since each...