BackgroundIn the field of neuroscience, neural modules and circuits that control biological functions have been found throughout entire neural networks. Correlations in neural activity can be used to identify such neural modules. Recent technological advances enable us to measure whole-brain neural activity with single-cell resolution in several species including\documentclass[12pt]{minimal}
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				\begin{document}$$Caenorhabditis\ elegans$$\end{document}Caenorhabditiselegans. Because current neural activity data inC. eleganscontain many missing data points, it is necessary to merge results from as many animals as possible to obtain more reliable functional modules.ResultsIn this work, we developed a new time-series clustering method, WormTensor, to identify functional modules using whole-brain activity data fromC. elegans. WormTensor uses a distance measure, modified shape-based distance to account for the lags and the mutual inhibition of cell–cell interactions and applies the tensor decomposition algorithm multi-view clustering based on matrix integration using the higher orthogonal iteration of tensors (HOOI) algorithm (MC-MI-HOOI), which can estimate both the weight to account for the reliability of data from each animal and the clusters that are common across animals.ConclusionWe applied the method to 24 individualC. elegansand successfully found some known functional modules. Compared with a widely used consensus clustering method to aggregate multiple clustering results, WormTensor showed higher silhouette coefficients. Our simulation also showed that WormTensor is robust to contamination from noisy data. WormTensor is freely available as an R/CRAN packagehttps://cran.r-project.org/web/packages/WormTensor.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12859-023-05230-2.