Somtimes: self organizing maps for time series clustering and its application to serious illness conversations.

Autor: Javed A; Department of Medicine, Stanford University, 300 Pasteur Dr, Stanford, CA 94305 USA.; Department of Computer Science, University of Vermont, Burlington, VT USA., Rizzo DM; Department of Civil and Environmental Engineering, University of Vermont, Burlington, VT USA.; Department of Computer Science, University of Vermont, Burlington, VT USA., Lee BS; Department of Computer Science, University of Vermont, Burlington, VT USA., Gramling R; Department of Family Medicine, University of Vermont, Burlington, VT USA.
Jazyk: angličtina
Zdroj: Data mining and knowledge discovery [Data Min Knowl Discov] 2024; Vol. 38 (3), pp. 813-839. Date of Electronic Publication: 2023 Oct 20.
DOI: 10.1007/s10618-023-00979-9
Abstrakt: There is demand for scalable algorithms capable of clustering and analyzing large time series data. The Kohonen self-organizing map (SOM) is an unsupervised artificial neural network for clustering, visualizing, and reducing the dimensionality of complex data. Like all clustering methods, it 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 that accommodates distortions when aligning time series. Despite its popularity in clustering, DTW is limited in practice because the runtime complexity is quadratic with the length of the time series. To address this, we present a new a self-organizing map for clustering TIME Series, called SOMTimeS, which uses DTW as the distance measure. The method has similar accuracy compared with other DTW-based clustering algorithms, yet scales better and runs faster. The computational performance stems from the pruning of unnecessary DTW computations during the SOM's training phase. For comparison, we implement a similar pruning strategy for K-means, and call the latter K-TimeS. SOMTimeS and K-TimeS pruned 43% and 50% of the total DTW computations, respectively. Pruning effectiveness, accuracy, execution time and scalability are evaluated using 112 benchmark time series datasets from the UC Riverside classification archive, and show that for similar accuracy, a 1.8 × speed-up on average for SOMTimeS and K-TimeS, respectively with that rates vary between 1 × and 18 × depending on the dataset. We also apply SOMTimeS to a healthcare study of patient-clinician serious illness conversations to demonstrate the algorithm's utility with complex, temporally sequenced natural language.
Supplementary Information: The online version contains supplementary material available at 10.1007/s10618-023-00979-9.
Competing Interests: Conflict of interestThe authors have no relevant financial or non-financial interests to disclose.
(© The Author(s) 2023.)
Databáze: MEDLINE
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