Abstract
This paper presents an approach for mining fuzzy Association Rules (ARs) relating the properties of composite items, i.e. items that each feature a number of values derived from a common schema. We partition the values associated to properties into fuzzy sets in order to apply fuzzy Association Rule Mining (ARM). This paper describes the process of deriving the fuzzy sets from the properties associated to composite items and a unique Composite Fuzzy Association Rule Mining (CFARM) algorithm founded on the certainty factor interestingness measure to extract fuzzy association rules. The paper demonstrates the potential of composite fuzzy property ARs, and that a more succinct set of property ARs can be produced using the proposed approach than that generated using a non-fuzzy method.
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Khan, M.S., Muyeba, M., Coenen, F. (2008). Mining Fuzzy Association Rules from Composite Items. In: Bramer, M. (eds) Artificial Intelligence in Theory and Practice II. IFIP AI 2008. IFIP – The International Federation for Information Processing, vol 276. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-09695-7_7
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DOI: https://doi.org/10.1007/978-0-387-09695-7_7
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-09694-0
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