  We set out the novel bottom up procedure to aggregate or cluster cells with
small frequency counts together, in a two way classification while maintaining
dependence in the table. The procedure is model free. It combines cells in a
table into clusters based on independent log odds ratios. We use this procedure
to build a set of statistically efficient and robust imputation cells, for the
imputation of missing values of a continuous variable using a pair
classification variables. A nice feature of the procedure is it forms
aggregation groups homogeneous with respect to the cell response mean. Using a
series of simulation studies, we show IlocA only groups together independent
cells and does so in a consistent and credible way. While imputing missing
data, we show IlocAs generates close to an optimal number of imputation cells.
For ignorable non-response the resulting imputed means are accurate in general.
With non-ignorable missingness results are consistent with those obtained
elsewhere. We close with a case study applying our method to imputing missing
building energy performance data
