Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Collective Mining of Bayesian Networks from Distributed Heterogeneous Data
Knowledge and Information Systems
A high-performance distributed algorithm for mining association rules
Knowledge and Information Systems
Learning dynamic Bayesian network models via cross-validation
Pattern Recognition Letters
Fast and exact out-of-core and distributed k-means clustering
Knowledge and Information Systems
Learning Bayesian Networks
Data quality awareness: a case study for cost optimal association rule mining
Knowledge and Information Systems - Special Issue on Mining Low-Quality Data
Expectation Correction for Smoothed Inference in Switching Linear Dynamical Systems
The Journal of Machine Learning Research
A Bayesian network approach to explaining time series with changing structure
Intelligent Data Analysis
Temporal data mining approaches for sustainable chiller management in data centers
ACM Transactions on Intelligent Systems and Technology (TIST)
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A dynamic Bayesian network (DBN) is one of popular approaches for relational knowledge discovery such as modeling relations or dependencies, which change over time, between variables of a dynamic system. In this paper, we propose an adaptive learning method (autoDBN) to learn DBNs with changing structures from multivariate time series. In autoDBN, segmentation of time series is achieved first through detecting geometric structures transformed from time series, and then model regions are found from the segmentation by designed finding strategies; in each found model region, a DBN model is established by existing structure learning methods; finally, model revisiting is developed to refine model regions and improve DBN models. These techniques provide a special mechanism to find accurate model regions and discover a sequence of DBNs with changing structures, which are adaptive to changing relations between multivariate time series. Experimental results on simulated and real time series show that autoDBN is very effective in finding accurate/reasonable model regions and gives lower error rates, outperforming the switching linear dynamic system method and moving window method.