  Correspondence analysis (CA) is a popular technique to visualize the
relationship between two categorical variables. CA uses the data from a two-way
contingency table and is affected by the presence of outliers. The
supplementary points method is a popular method to handle outliers. Its
disadvantage is that the information from entire rows or columns is removed.
However, outliers can be caused by cells only. In this paper, a reconstitution
algorithm is introduced to cope with such cells. This algorithm can reduce the
contribution of cells in CA instead of deleting entire rows or columns. Thus
the remaining information in the row and column involved can be used in the
analysis. The reconstitution algorithm is compared with two alternative methods
for handling outliers, the supplementary points method and MacroPCA. It is
shown that the proposed strategy works well.
