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

Document Type

Conference Proceeding

Publisher

Springer Verlag

Faculty

Faculty of Computing, Health and Science

School

School of Computer and Security Science

RAS ID

5958

Comments

Kordic, S., Lam, C. 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. Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence. PRICAI '08. (pp. 985-990). Berlin, Germany: Springer Verlag. Available here

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.

DOI

10.1007/978-3-540-89197-0_95

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Link to publisher version (DOI)

10.1007/978-3-540-89197-0_95