Optimal architecture-independent scheduling of fine-grain tree-sweep computations
SPDP '95 Proceedings of the 7th IEEE Symposium on Parallel and Distributeed Processing
Search bound strategies for rule mining by iterative deepening
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
Predicting glaucomatous visual field deterioration through short multivariate time series modelling
Artificial Intelligence in Medicine
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The Multi-Stream Dependency Detection algorithm finds rules that capture statistical dependencies between patterns in multivariate time series of categorical data [Oates and Cohen, 1996c]. Rule strength is measured by the G statistic [Wickens, 1989], and an upper bound on the value of G for the descendants of a node allows MSDD'S search space to be pruned. However, in the worst case, the algorithm will explore exponentially many rules. This paper presents and empirically evaluates two ways of addressing this problem. The first is a set of three methods for reducing the size of MSDD'S search space based on information collected during the search process. Second, we discuss an implementation of MSDD that distributes its computations over multiple machines on a network.