A Comparison of Clustering and Missing Data Methods for Health Sciences
This work addresses data analysis challenges in health sciences, but it is incremental as it combines existing techniques for specific applications.
The paper tackled clustering and missing data problems in health behavior research by proposing compressive sensing matrix completion with spectral clustering, achieving lower misclassification rates and better matrix completion performance compared to standard methods like LPA and FIML.
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.