Mining Trajectories of Laboratory Data using Multiscale Matching and Clustering

This paper shows application of trajectory mining techniques to laboratory examination data, where dynamics of two laboratory examinations are represented as a trajectory in a two dimensional plane. Each trajectory is segmented and compared with other trajectories by multiscale matching, and clustered by using similarities obtained by matching technique. Experimental results on chronic hepatitis data demonstrated that the method could find the groups of trajectories which reflects temporal covariance of platelet, albumin and choline esterase.