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Drug Safety

, Volume 30, Issue 7, pp 621–622 | Cite as

Principles of Data Mining

  • David J. HandEmail author
Conference Paper

Abstract

Data mining is the discovery of interesting, unexpected or valuable structures in large datasets. As such, it has two rather different aspects. One of these concerns large-scale, ‘global’ structures, and the aim is to model the shapes, or features of the shapes, of distributions. The other concerns small-scale, ‘local’ structures, and the aim is to detect these anomalies and decide if they are real or chance occurrences. In the context of signal detection in the pharmaceutical sector, most interest lies in the second of the above two aspects; however, signal detection occurs relative to an assumed background model, therefore, some discussion of the first aspect is also necessary. This paper gives a lightning overview of data mining and its relation to statistics, with particular emphasis on tools for the detection of adverse drug reactions.

Keywords

Data Mining Adverse Drug Reaction Pattern Discovery Selectivity Bias Poor Quality Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

No sources of funding were used to assist in the preparation of this paper. The author has no conflicts of interest that are directly relevant to the content of this paper.

References

  1. 1.
    Hand DJ, Manila H, Smyth P. Principles of data mining. Cambridge (MA): The MIT Press, 2001Google Scholar
  2. 2.
    Hand DJ, Blunt G, Kelly MG, et al. Data mining for fun and profit. Stat Sci 2000; 15(2): 111–31CrossRefGoogle Scholar

Copyright information

© Adis Data Information BV 2007

Authors and Affiliations

  1. 1.Department of Mathematics, South Kensington CampusImperial College LondonLondonUK

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