Graph Visualization and Navigation in Information Visualization: A Survey
IEEE Transactions on Visualization and Computer Graphics
Bayesian hierarchical clustering
ICML '05 Proceedings of the 22nd international conference on Machine learning
Computers and Operations Research
Multi-HDP: a non parametric Bayesian model for tensor factorization
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Group sparse priors for covariance estimation
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Computer Science Review
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Vector Auto-regressive (VAR) models are useful for analyzing temporal dependencies among multivariate time series, known as Granger causality. There exist methods for learning sparse VAR models, leading directly to causal networks among the variables of interest. Another useful type of analysis comes from clustering methods, which summarize multiple time series by putting them into groups. We develop a methodology that integrates both types of analyses, motivated by the intuition that Granger causal relations in real-world time series may exhibit some clustering structure, in which case the estimation of both should be carried out together. Our methodology combines sparse learning and a nonparametric bi-clustered prior over the VAR model, conducting full Bayesian inference via blocked Gibbs sampling. Experiments on simulated and real data demonstrate improvements in both model estimation and clustering quality over standard alternatives, and in particular biologically more meaningful clusters in a T-cell activation gene expression time series dataset than those by other methods.