Abstract
Sequential rule mining is a well-established data mining technique for binary valued data. Many variations have been proposed, most approaches use the support-confidence-lift framework. Existing approaches make assumptions concerning the definition of what a sequence is. However, this definition is application dependent. In this paper we look at sequential rule mining with respect to multi-morbidity disease prediction which entails a rethink of the definition of what a sequence is, and a consequent rethink of the operation of the support-confidence-lift framework. A novel sequential rule mining algorithm is proposed designed to address the challenge of multi-morbidity disease prediction. The SEquential RElational N-DIsease Pattern (SERENDIP) algorithm.
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Vincent-Paulraj, A., Burnside, G., Coenen, F., Pirmohamed, M., Walker, L. (2021). Sequential Association Rule Mining Revisited: A Study Directed at Relational Pattern Mining for Multi-morbidity. In: Bramer, M., Ellis, R. (eds) Artificial Intelligence XXXVIII. SGAI-AI 2021. Lecture Notes in Computer Science(), vol 13101. Springer, Cham. https://doi.org/10.1007/978-3-030-91100-3_20
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