We proposed a method to extract causal relations of clusters from multi-dimensional event sequence data, along with another method to detect the changes of the extracted relations over time. The proposed Granger Cluster Sequence Mining (GCSM) algorithm identifies the pairs of spatial data clusters that have causality over time with each other. It extends the Cluster Sequence Mining algorithm, which utilized a statistical inference technique to identify occurrence relation, with a causality based on Granger causality. We also proposed a statistical model to infer the changes over time of each extracted causal relation. With experiments using synthetic data and semi-real data, we confirmed that the algorithm works correctly, and able to extract the embedded causal relations with high F-score and high accuracy of change points.

This paper proposed a method to detect changes in causal relations over a multi-dimensional sequence of events. Cluster Sequence Mining algorithm was modified to extract causal relations in the form of g-patterns: a pair of clusters of events that have their occurrence time determined by Granger causality. This paper also proposed the pattern time signature, a probabilistic density function of the cluster sequence occurring at any given time. Synthetic data were used for validation. The result shows that the proposed algorithm can correctly identify the changes in causal relations even under noisy data.

ART-2a has been shown to be effective against stream data clustering with unknown number of cluster in nature. As data grows, ART-2a running time become a major problem. We proposed a new algorithm, ART-2b, whose runtime performance is linear to the number of input instances, while still maintaining similar clustering result to ART-2a.