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Data Mining of Missing Persons Data

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Classification and Clustering for Knowledge Discovery

Part of the book series: Studies in Computational Intelligence ((SCI,volume 4))

Abstract

This paper presents the results of analysis to evaluate the effectiveness of data mining techniques to predict the outcome for missing persons cases. A rulebased system is used to derive augmentations to supplement police officer intuition. Results indicate that rule-based systems can effectively identify variables for prediction.

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Authors and Affiliations

  1. School of Information Technology, Charles Sturt University, NSW 2795, Australia

    K. Blackmore & T. Bossomaier

  2. School of Social Sciences and Liberal Studies, Charles Sturt University, NSW 2795, Australia

    S. Foy & D. Thomson

Authors
  1. K. Blackmore
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  2. T. Bossomaier
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  3. S. Foy
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  4. D. Thomson
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Editor information

Saman K. Halgamuge Lipo Wang

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Blackmore, K., Bossomaier, T., Foy, S., Thomson, D. Data Mining of Missing Persons Data. In: K. Halgamuge, S., Wang, L. (eds) Classification and Clustering for Knowledge Discovery. Studies in Computational Intelligence, vol 4. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11011620_19

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  • DOI: https://doi.org/10.1007/11011620_19

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26073-8

  • Online ISBN: 978-3-540-32404-1

  • eBook Packages: EngineeringEngineering (R0)

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