Model-Based Clustering of Incomplete Data

TL;DRAbstract

Several important questions have yet to be answered concerning clustering incomplete data. For example, how can disparate solutions from multiply imputed cluster results be resolved? Additionally, can a model-selection criterion be developed which can detect the correct number of LCA classes after multiple imputation has been performed? Finally, as cluster analysis depends on measures of uncertainty, what is the eect of missing values on such measures? This thesis presents new theorems, methodologies, and applications which demonstrate solutions to these pressing questions.

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