Clustering multivariate functional data using unsupervised binary trees
This work provides an incremental clustering method for researchers working with complex functional data, offering easily interpretable results and fast predictions.
This paper proposes a model-based clustering algorithm for multivariate functional data, including curves and images, which can be measured with error at discrete points. The algorithm constructs binary trees through recursive splitting to determine the number of groups in a data-driven manner, demonstrating good performance on simulated datasets and application to vehicle trajectories.
We propose a model-based clustering algorithm for a general class of functional data for which the components could be curves or images. The random functional data realizations could be measured with error at discrete, and possibly random, points in the definition domain. The idea is to build a set of binary trees by recursive splitting of the observations. The number of groups are determined in a data-driven way. The new algorithm provides easily interpretable results and fast predictions for online data sets. Results on simulated datasets reveal good performance in various complex settings. The methodology is applied to the analysis of vehicle trajectories on a German roundabout.