Semi-supervised Clustering for Sparsely Sampled Longitudinal Data

Longitudinal data studies track the measurements of individual subjects over time. The features of the hidden classes in longitudinal data can be effectively extracted by clustering. In practice, however, longitudinal data analysis is hampered by the sparse sampling and different sampling points among subjects. These problems have been overcome by adopting a functional clustering data approach for sparsely sampled data, but this approach is unsuitable when the difference between classes is small. Therefore, we propose a semi-supervised approach for clustering sparsely sampled longitudinal data in which the clustering result is aided and biased by certain labeled subjects. The effectiveness of the proposed method was evaluated in simulation. The proposed method proved especially effective even when the difference between classes is blurred by interference such as noise. In summary, by adding some subjects with class information, we can enhance existing information to realize successful clustering.