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Irregularly sampled time series data are common in a variety of fields. Manytypical methods for drawing insight from data fail in this case. Here weattempt to generalize methods for clustering trajectories to irregularly andsparsely sampled data. We first construct synthetic data sets, then propose andassess four methods of data alignment to allow for application of spectralclustering. We also repeat the same process for real data drawn from medicalrecords of patients with sickle cell disease -- patients whose subjectiveexperiences of pain were tracked for several months via a mobile app.  We find that different methods for aligning irregularly sampled sparse datasets can lead to different optimal numbers of clusters, even for synthetic datawith known properties. For the case of sickle cell disease, we find that threeclusters is a reasonable choice, and these appear to correspond to (1) a lowpain group with occasionally acute pain, (2) a group which experiences moderatemean pain that fluctuates often from low to high, and (3) a group thatexperiences persistent high levels of pain.  Our results may help physicians and patients better understand and managepatients' pain levels over time, and we expect that the methods we develop willapply to a wide range of other data sources in medicine and beyond.

Clustering of Pain Dynamics in Sickle Cell Disease from Sparse, Uneven Samples