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Naïve Rules Do Not Consider Underlying Causality

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Book cover Data Mining: Foundations and Practice

Part of the book series: Studies in Computational Intelligence ((SCI,volume 118))

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Summary

Naïve association rules may result if the underlying causality of the rules is not considered. The greatest impact on the decision value quality of association rules may come from treating association rules as causal statements without understanding whether there is, in fact, underlying causality. A complete knowledge of all possible factors (i.e., states, events, constraints) might lead to a crisp description of whether an effect will occur. However, it is unlikely that all possible factors can be known. Commonsense understanding and reasoning accepts imprecision, uncertainty and imperfect knowledge. The events in an event/effect complex may be incompletely known; as well as, what constraints and laws the complex is subject to. Usually, commonsense reasoning is more successful in reasoning about a few large-grain sized events than many fine-grained events. A satisficing solution would be to develop large-grained solutions and only use the finer-grain when the impreciseness of the large-grain is unsatisfactory.

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Authors and Affiliations

  1. Applied Artificial Intelligence Laboratory, University of Cincinnati, Cincinnati, OH, 45221-0030, USA

    Lawrence J. Mazlack

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  1. Lawrence J. Mazlack
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Editors and Affiliations

  1. Department of Computer Science, San Jose State University, San Jose, CA, 95192, USA

    Tsau Young Lin

  2. Department of Computer Science and Information Systems, Kennesaw State University, Building 11, Room 3060 1000 Chastain Road, Kennesaw, GA, 30144, USA

    Ying Xie

  3. Department of Computer Science, The University at Stony Brook, Stony Brook, New York, 11794-4400, USA

    Anita Wasilewska

  4. Institute of Information Science, Academia Sinica, No 128, Academia Road, Section 2 Nankang, Taipei, 11529, Taiwan

    Churn-Jung Liau

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Mazlack, L.J. (2008). Naïve Rules Do Not Consider Underlying Causality. In: Lin, T.Y., Xie, Y., Wasilewska, A., Liau, CJ. (eds) Data Mining: Foundations and Practice. Studies in Computational Intelligence, vol 118. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78488-3_13

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  • DOI: https://doi.org/10.1007/978-3-540-78488-3_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-78487-6

  • Online ISBN: 978-3-540-78488-3

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