Abstract
This chapter looks at the problem of finding any rules of interest that can be derived from a given dataset, not just classification rules as before. This is known as Association Rule Mining or Generalised Rule Induction. A number of measures of rule interestingness are defined and criteria for choosing between measures are discussed. An algorithm for finding the best \(N\) rules that can be generated from a dataset using the \(J\)-measure of the information content of a rule and a ‘beam search’ strategy is described.
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References
Piatetsky-Shapiro, G. (1991). Discovery, analysis and presentation of strong rules. In G. Piatetsky-Shapiro & W. J. Frawley (Eds.), Knowledge discovery in databases (pp. 229–248). Menlo Park: AAAI Press.
Smyth, P., & Goodman, R. M. (1992). Rule induction using information theory. In G. Piatetsky-Shapiro & W. J. Frawley (Eds.), Knowledge discovery in databases (pp. 159–176). Menlo Park: AAAI Press.
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Bramer, M. (2016). Association Rule Mining I. In: Principles of Data Mining. Undergraduate Topics in Computer Science. Springer, London. https://doi.org/10.1007/978-1-4471-7307-6_16
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DOI: https://doi.org/10.1007/978-1-4471-7307-6_16
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