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

Abstract

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$$\times$$ × speed-up on average for SOMTimeS and K-TimeS, respectively with that rates vary between 1$$\times$$ × and 18$$\times$$ × 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.

Machine Learning Prediction Techniques in the Optimization of Diagnostic Laboratories’ Network Operations

“…Sci. 2024, 14, 2429 6 of 23 relationships [7]. SOMs can identify similar patterns in temporal sequences and group them based on their similarity characteristics.…”

“…They can cluster and analyze patterns within these data. The process of clustering sequence data using SOMs involves training a SOM map on the data to represent the data's structure and relationships [7]. SOMs can identify similar patterns in temporal sequences and group them based on their similarity characteristics.…”

Enhancing Warehouse Efficiency With Time Series Clustering: A Hybrid Storage Location Assignment Strategy

“…Our goal is to uncover and visualize meaningful patterns in the order time series data, emphasizing the preservation of temporal relationships and the accurate representation of order clusters. During the training process, the SOM dynamically adjusts its nodes to minimize the combined objective function, facilitating the identification of clusters of orders with similar temporal characteristics [22]. The grid size we used for SOM is the square of dataset size.…”