Efficient Mining of Statistical Dependencies
Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence. Machine Learning. Pages 794-798.
Abstract
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.
@inproceedings{ijcai1999p113,
title = {Efficient Mining of Statistical Dependencies},
author = {Oates, Tim and Schmill, Matthew D. and Cohen, Paul R.},
booktitle = {Proceedings of the Sixteenth International Joint Conference on
Artificial Intelligence, {IJCAI-99}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Thomas Dean},
pages = {794--798},
year = {1999},
note = {Machine Learning},
url = {https://ijcai.org/proceedings/1999/113},
}
TY - CPAPER TI - Efficient Mining of Statistical Dependencies AU - Oates, Tim AU - Schmill, Matthew D. AU - Cohen, Paul R. T2 - Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, IJCAI-99 PB - International Joint Conferences on Artificial Intelligence PY - 1999 SP - 794 EP - 798 UR - https://ijcai.org/proceedings/1999/113 ER -Copied
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