Abstract
We investigate the problem of mining interesting association rules over a pair of categorical attributes at any level of data granularity. We do this by integrating the rule discovery process with a form of clustering. This allows associations between groups of ;items to be formed where the groping of items is based on maximising the “interestingness” of the associations discovered. Previous work on mining generalised associations assumes either a distance metric on the attribute values or a taxonomy over the items mined. These methods use the metric/taxonomy to limit the space of possible associations that can be found. We develop a measure of the interestingness of association rules based on support and the dependency between the item sets and use this measure to guide the search. We apply the method to a data set and observe the extraction of “interesting” associations. This method could allow interesting and unexpected associations to be discovered as the search space is not being limited by user defined hierarchies.
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© 1998 Springer-Verlag Berlin Heidelberg
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Gray, B., Orlowska, M.E. (1998). CCAIIA: Clustering categorical attributes into interesting association rules. In: Wu, X., Kotagiri, R., Korb, K.B. (eds) Research and Development in Knowledge Discovery and Data Mining. PAKDD 1998. Lecture Notes in Computer Science, vol 1394. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-64383-4_12
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DOI: https://doi.org/10.1007/3-540-64383-4_12
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