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Mining the knowledge mine

The hot spots methodology for mining large real world databases

  • Machine Learning
  • Conference paper
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Advanced Topics in Artificial Intelligence (AI 1997)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1342))

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Abstract

As databases grow in size and complexity the task of adding value to the wealth of data becomes difficult. Data mining has emerged as the technology to add value to enormous databases by finding new and important snippets (or nuggets) of knowledge. With large training sets, however, extremely large collections of nuggets are being extracted, leading to much “fools gold” amongst which to fossick for the real gold. Attention is now being directed towards the problem of how to better focus on the most precious nuggets. This paper presents the hot spots methodology, adopting a multi-strategy and interactive approach to help focus on the important nuggets. The methodology first performs data mining and then explores the resulting models to find the important nuggets contained therein. This approach is demonstrated in insurance and fraud applications.

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References

  • Fayyad, U. M., Piatetsky-Shapiro, G. and Smyth, P.: 1996, From data mining to knowledge discovery: An overview, in U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth and R. Uthurusamy (eds), Advances in Knowledge Discovery and Data Mining, AAAI Press / The MIT Press, pp. 1–34.

    Google Scholar 

  • Huang, Z.: 1997, Clustering large data sets with mixed numeric and categorical values, in H.-J. Lu, H. Liu and H. Motoda (eds), Knowledge discovery and data mining: techniques and applications, World Scientific.

    Google Scholar 

  • Mallows, C. and Pregibon, D.: 1996, The analysis of call-detail data, The Sydney International Statistical Congress.

    Google Scholar 

  • Quinlan, J. R.: 1993, C4.5: Programs for Machine Learning, Morgan Kaufmann, San Mateo, CA, 1993., Morgan Kaufmann, San Mateo, CA.

    Google Scholar 

  • Viveros, M. S., Nearhos, J. P. and Rothman, M. J.: 1996, Applying data mining techniques to a health insurance information system, Proceedings of the 22nd VLDB Conference, Mumbai (Bombay), India, pp. 286–293.

    Google Scholar 

  • Williams, G. J. and Huang, Z.: 1996, A case study in knowledge acquisition for insurance risk assessment using a kdd methodology, in P. Compton, R. Mizoguchi, H. Motoda and T. Menzies (eds), Pacific Knowledge Acquisition Workshop, pp. 117–129.

    Google Scholar 

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Abdul Sattar

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© 1997 Springer-Verlag Berlin Heidelberg

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Williams, G.J., Huang, Z. (1997). Mining the knowledge mine. In: Sattar, A. (eds) Advanced Topics in Artificial Intelligence. AI 1997. Lecture Notes in Computer Science, vol 1342. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63797-4_87

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  • DOI: https://doi.org/10.1007/3-540-63797-4_87

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63797-4

  • Online ISBN: 978-3-540-69649-0

  • eBook Packages: Springer Book Archive

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