  Let n respondents rank order d items, and suppose that d << n. Our main task
is to uncover and display the structure of the observed rank data by an
exploratory riffle shuffling procedure which sequentially decomposes the n
voters into a finite number of coherent groups plus a noisy group : where the
noisy group represents the outlier voters and each coherent group is composed
of a finite number of coherent clusters. We consider exploratory riffle
shuffling of a set of items to be equivalent to optimal two blocks seriation of
the items with crossing of some scores between the two blocks. A riffle
shuffled coherent cluster of voters within its coherent group is essentially
characterized by the following facts : a) Voters have identical first TCA
factor score, where TCA designates taxicab correspondence analysis, an L1
variant of correspondence analysis ; b) Any preference is easily interpreted as
riffle shuffling of its items ; c) The nature of different riffle shuffling of
items can be seen in the structure of the contingency table of the first-order
marginals constructed from the Borda scorings of the voters ; d) The first TCA
factor scores of the items of a coherent cluster are interpreted as Borda scale
of the items. We also introduce a crossing index, which measures the extent of
crossing of scores of voters between the two blocks seriation of the items. The
novel approach is explained on the benchmarking SUSHI data set, where we show
that this data set has a very simple structure, which can also be communicated
in a tabular form.
