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.
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Kordic, S., Lam, P., Xiao, J., Li, H. (2008). A Data-Driven Approach for Finding the Threshold Relevant to the Temporal Data Context of an Alarm of Interest. In: Ho, TB., Zhou, ZH. (eds) PRICAI 2008: Trends in Artificial Intelligence. PRICAI 2008. Lecture Notes in Computer Science(), vol 5351. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89197-0_95
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DOI: https://doi.org/10.1007/978-3-540-89197-0_95
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