The iVAT (asiVAT) algorithms reorder symmetric (asymmetric) dissimilarity data so that an image of the data may reveal cluster substructure. Images formed from incomplete data don't offer a very rich interpretation of cluster structure. In this paper we examine four methods for completing the input data with imputed values before imaging. We choose a best method using contaminated versions of the complete Iris data, for which the desired results are known. Then we analyse two real world data

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... s from social networks that are incomplete using the best imputation method chosen in the juried trials with Iris: (i) Sampson's monastery data, an incomplete, asymmetric relation matrix; and (ii) the karate club data, comprising a symmetric similarity matrix that is about 86% incomplete.