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There is an increasing demand for scalable algorithms capable of clusteringand analyzing large time series datasets. The Kohonen self-organizing map (SOM)is a type of unsupervised artificial neural network for visualizing andclustering complex data, reducing the dimensionality of data, and selectinginfluential features. Like all clustering methods, the SOM requires a measureof similarity between input data (in this work time series). Dynamic timewarping (DTW) is one such measure, and a top performer given that itaccommodates the distortions when aligning time series. Despite its use inclustering, DTW is limited in practice because it is quadratic in runtimecomplexity with the length of the time series data. To address this, we presenta new DTW-based clustering method, called SOMTimeS (a Self-Organizing Map forTIME Series), that scales better and runs faster than other DTW-basedclustering algorithms, and has similar performance accuracy. The computationalperformance of SOMTimeS stems from its ability to prune unnecessary DTWcomputations during the SOM's training phase. We also implemented a similarpruning strategy for K-means for comparison with one of the top performingclustering algorithms. We evaluated the pruning effectiveness, accuracy,execution time and scalability on 112 benchmark time series datasets from theUniversity of California, Riverside classification archive. We showed that forsimilar accuracy, the speed-up achieved for SOMTimeS and K-means was 1.8x onaverage; however, rates varied between 1x and 18x depending on the dataset.SOMTimeS and K-means pruned 43% and 50% of the total DTW computations,respectively. We applied SOMtimeS to natural language conversation datacollected as part of a large healthcare cohort study of patient-clinicianserious illness conversations to demonstrate the algorithm's utility withcomplex, temporally sequenced phenomena.

SOMTimeS: Self Organizing Maps for Time Series Clustering and its Application to Serious Illness Conversations