Abstract
This paper continues the study of mining patterns from the real world data. Association rules that respects the semantics modeled by binary relations are called binary semantic association rules. By experiments we find that semantic computation is necessary, efficient and fruitful. It is necessary, because we find the supports of length 2 candidate is quite high in randomly generated data. It is efficient, because the checking of semantics constraints occurs only at length 2. It is fruitful the additional cost is well compensated by the saving in pruning away (non-semantic) association rules.
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Lin, T.Y., Louie, E. (2003). Association Rules with Additional Semantics Modeled by Binary Relations. In: Inuiguchi, M., Hirano, S., Tsumoto, S. (eds) Rough Set Theory and Granular Computing. Studies in Fuzziness and Soft Computing, vol 125. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-36473-3_14
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DOI: https://doi.org/10.1007/978-3-540-36473-3_14
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