A Data-Driven Approach for Finding the Threshold Relevant to the Temporal Data Context of an Alarm of Interest

Authors:
Savo Kordic;Peng Lam;Jitian Xiao;Huaizhong Li
Affiliations:
The School of Computer and Information Science, Edith Cowan University, Perth, Western Australia 6050;The School of Computer and Information Science, Edith Cowan University, Perth, Western Australia 6050;The School of Computer and Information Science, Edith Cowan University, Perth, Western Australia 6050;The School of Computer Science and Engineering, Wenzhou University Town, Zhejiang, China 325035
Venue:
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Year:
2008

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Abstract

A typical chemical alarm database is characterized by a large search space with skewed frequency distribution. Thus in practice, discovery of alarm patterns and interesting associations from such data can be exceptionally difficult and costly. To overcome this problem we propose a data-driven approach to optimally derive the pruning thresholds which are relevant to the temporal data context of the particular tag of interest.