A fundamental goal of educational research is identifying students' current stage of skill mastery (complete/partial/none). In recent years a number of cognitive diagnosis models have become a popular means of estimating student skill knowledge. However, these models become difficult to estimate as the number of students, items, and skills grows. There exist alternatives such as sum-scores and the capability matrix. While initial theoretical work on sum-scores has been done, the behavior of sum-scores and the capability matrix is not well understood with respect to each other or to estimates from cognitive diagnosis models. In this paper we compare the performance of the three estimates of student skill knowledge under a variety of clustering methods using simulated data with varying levels of missing values. (Contains 6 tables.) [For the complete proceedings, "Proceedings of the International Conference on Educational Data Mining (EDM) (2nd, Cordoba, Spain, July 1-3, 2009)," see ED539041.]