Spatial-Temporal Scientific Data Clustering via Deep Convolutional Neural Network

I explore the usage of deep convolutional neural network for clustering the time steps of a spatial-temporal scientific dataset. The approach first takes the scientific dataset as training data and trains a deep convolutional autoencoder. A low-dimensional feature space or latent space can be extracted by inferencing the encoding part of the network. As a result, each time step is transformed into a feature descriptor that can be compared with each other in the feature space. In this way, we can cluster time steps according to their feature descriptors, and each group of time steps has a similar characterization. We demonstrate the effectiveness of our approach using a real-world simulation dataset of water contamination. Multiple variables and their combinations of this dataset are fed into our approach. The trained network enables the clustering of the time steps and facilitates scientists to examine their large spatial-temporal datasets.

we demonstrate the feasibility to use autoencoder with deep convolutional neural network to cluster the time steps of a scientific dataset. Our preliminary results show that feature descriptors can be learned for individual variable and their combinations for a real-world simulation data. Each time step can be represented as a set of vectors in the feature space. Using this representation, we can quantify the distance between the time steps and cluster them into different groups, where each group has similar patterns. Our visualization results qualitatively reveal the difference and the similarity among different classes.