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We set out the novel bottom up procedure to aggregate or cluster cells withsmall frequency counts together, in a two way classification while maintainingdependence in the table. The procedure is model free. It combines cells in atable into clusters based on independent log odds ratios. We use this procedureto build a set of statistically efficient and robust imputation cells, for theimputation of missing values of a continuous variable using a pairclassification variables. A nice feature of the procedure is it formsaggregation groups homogeneous with respect to the cell response mean. Using aseries of simulation studies, we show IlocA only groups together independentcells and does so in a consistent and credible way. While imputing missingdata, 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 obtainedelsewhere. We close with a case study applying our method to imputing missingbuilding energy performance data

IlocA: An algorithm to Cluster Cells and form Imputation Groups from a pair of Classification Variables