Abstract
Identifying and expressing data patterns in form of association rules is a commonly used technique in data mining. Typically, association rules discovery is based on two criteria: support and confidence. In this paper we will briefly discuss the insufficiency on these two criteria, and argue the importance of including interestingness/dependency as a criterion for (association) pattern discovery. From the practical computational perspective, we will show how the proposed criterion grounded on interestingness could be used to improve the efficiency of pattern discovery mechanism. Furthermore, we will show a probabilistic inference mechanism that provides an alternative to pattern discovery. Example illustration and preliminary study for evaluating the proposed approach will be presented.
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Sy, B.K. (2003). Discovering Association Patterns Based on Mutual Information. In: Perner, P., Rosenfeld, A. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2003. Lecture Notes in Computer Science, vol 2734. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45065-3_32
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DOI: https://doi.org/10.1007/3-540-45065-3_32
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