Document Type
Conference Proceeding
Department
Mathematics (CMC), Community and Global Health (CGU), Mathematical Sciences (CGU)
Publication Date
4-22-2014
Abstract
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.
Rights Information
© 2014 Zhao, Needell, Johansen, Grenard
Terms of Use & License Information
Recommended Citation
Zhao, R., Needell, D., Johansen, C., Grenard, J. L., "A Comparison of Clustering and Missing Data Methods for Health Sciences", Proc. Asilomar Conference on Signals, Systems, and Computers, Pacific Grove CA, Nov. 2014. http://arxiv.org/abs/1404.5899
