  There is an increasing demand for scalable algorithms capable of clustering
and analyzing large time series datasets. The Kohonen self-organizing map (SOM)
is a type of unsupervised artificial neural network for visualizing and
clustering complex data, reducing the dimensionality of data, and selecting
influential features. Like all clustering methods, the SOM requires a measure
of similarity between input data (in this work time series). Dynamic time
warping (DTW) is one such measure, and a top performer given that it
accommodates the distortions when aligning time series. Despite its use in
clustering, DTW is limited in practice because it is quadratic in runtime
complexity with the length of the time series data. To address this, we present
a new DTW-based clustering method, called SOMTimeS (a Self-Organizing Map for
TIME Series), that scales better and runs faster than other DTW-based
clustering algorithms, and has similar performance accuracy. The computational
performance of SOMTimeS stems from its ability to prune unnecessary DTW
computations during the SOM's training phase. We also implemented a similar
pruning strategy for K-means for comparison with one of the top performing
clustering algorithms. We evaluated the pruning effectiveness, accuracy,
execution time and scalability on 112 benchmark time series datasets from the
University of California, Riverside classification archive. We showed that for
similar accuracy, the speed-up achieved for SOMTimeS and K-means was 1.8x on
average; 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 data
collected as part of a large healthcare cohort study of patient-clinician
serious illness conversations to demonstrate the algorithm's utility with
complex, temporally sequenced phenomena.
