A novel microarray value imputation method, HICCUP1, is presented. HICCUP improves upon existing value imputation methods in the several ways. (1) By judiciously integrating heterogeneous microarray datasets using hierarchical clustering, HICCUP overcomes the limitation of using only single dataset with limited number of samples; (2) Unlike local or global value imputation methods, by mining association rules, HICCUP selects appropriate subsets of the most relevant samples for better value imputation; and (3) by exploiting relationship among the sample space (e.g., cancer vs. non-cancer samples), HICCUP improves the accuracy of value imputation. Experiments with a real prostate cancer microarray dataset verify that HICCUP outperforms existing approaches.
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