  In this paper, we compare and analyze clustering methods with missing data in
health behavior research. In particular, we propose and analyze the use of
compressive sensing's matrix completion along with spectral clustering to
cluster health related data. The empirical tests and real data results show
that these methods can outperform standard methods like LPA and FIML, in terms
of lower misclassification rates in clustering and better matrix completion
performance in missing data problems. According to our examination, a possible
explanation of these improvements is that spectral clustering takes advantage
of high data dimension and compressive sensing methods utilize the
near-to-low-rank property of health data.
