Abstract
In managing medical data, handling time-series data, which contain irregularities, presents the greatest difficulty. In the present paper, we propose a first-order rule discovery method for handling such data. The present method is an attempt to use graph structure to represent time-series data and reduce the graph using specified rules for inducing hypothesis. In order to evaluate the proposed method, we conducted experiments using real-world medical data.
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Ichise, R., Numao, M. (2005). First-Order Rule Mining by Using Graphs Created from Temporal Medical Data. In: Tsumoto, S., Yamaguchi, T., Numao, M., Motoda, H. (eds) Active Mining. Lecture Notes in Computer Science(), vol 3430. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11423270_7
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DOI: https://doi.org/10.1007/11423270_7
Publisher Name: Springer, Berlin, Heidelberg
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