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Air pollution prediction with clustering-based ensemble of evolving spiking neural networks and a case study for London area.

, , , and . Environ. Model. Softw., (2019)

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A Survey on Data Mining Methods for Clustering Complex Spatiotemporal Data.. BDAS, volume 716 of Communications in Computer and Information Science, page 115-126. (2017)Discovery of closed spatio-temporal sequential patterns from event data., , and . KES, volume 159 of Procedia Computer Science, page 707-716. Elsevier, (2019)Effective air pollution prediction by combining time series decomposition with stacking and bagging ensembles of evolving spiking neural networks., , , , , and . Environ. Model. Softw., (December 2023)Air pollution prediction with clustering-based ensemble of evolving spiking neural networks and a case study for London area., , , and . Environ. Model. Softw., (2019)Unsupervised Anomaly Detection in Stream Data with Online Evolving Spiking Neural Networks., , , , and . CoRR, (2019)Efficient Discovery of Top-K Sequential Patterns in Event-Based Spatio-Temporal Data.. FedCSIS, volume 15 of Annals of Computer Science and Information Systems, page 47-56. (2018)Discovering Sequential Patterns in Event-Based Spatio-Temporal Data by Means of Microclustering - Extended Report.. CoRR, (2017)A Novel Breadth-first Strategy Algorithm for Discovering Sequential Patterns from Spatio-temporal Data., and . ICPRAM, page 459-466. SciTePress, (2019)A Framework for Discovering Frequent Event Graphs from Uncertain Event-based Spatio-temporal Data.. ICPRAM, page 656-663. SciTePress, (2019)Online Evolving Spiking Neural Networks for Incremental Air Pollution Prediction., , and . IJCNN, page 1-8. IEEE, (2020)