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Interactive Comprehensible Data Mining

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Ambient Intelligence for Scientific Discovery

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3345))

Abstract

In data mining, or knowledge discovery, we are essentially faced with a mass of data that we are trying to make sense of. We are looking for something “interesting”. Quite what “interesting” means is hard to define, however – one day it is the general trend that most of the data follows that we are intrigued by – the next it is why there are a few outliers to that trend. In order for a data mining to be generically useful to us, it must therefore have some way in which we can indicate what is interesting and what is not, and for that to be dynamic and changeable.

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Author information

Authors and Affiliations

  1. University of Birmingham, United Kingdom

    Andy Pryke & Russell Beale

Authors
  1. Andy Pryke
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  2. Russell Beale
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Editor information

Editors and Affiliations

  1. Ambient Intelligence Lab, CIC-2218, Carnegie Mellon University, 4720 Forbes Avenue, 15213, Pittsburgh, PA, USA

    Yang Cai

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Pryke, A., Beale, R. (2005). Interactive Comprehensible Data Mining. In: Cai, Y. (eds) Ambient Intelligence for Scientific Discovery. Lecture Notes in Computer Science(), vol 3345. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-32263-4_3

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  • DOI: https://doi.org/10.1007/978-3-540-32263-4_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-24466-0

  • Online ISBN: 978-3-540-32263-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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