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Extracting knowledge from data using an intelligent fuzzy data browser

  • Machine Learning and Data Mining
  • Conference paper
  • First Online:
Fuzzy Logic in Artificial Intelligence Towards Intelligent Systems (FLAI 1995)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1188))

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Abstract

Knowledge, in the form of rules, can enhance raw data by offering a compact summary or by giving a predictive capability. Frequently, information in a database may be incomplete or uncertain; however, it is often possible to estimate the value of missing data by comparison to similar cases in the database. Humans usually prefer to work in terms of rules which summarise trends in data, rather than remembering specific details from all cases. These rules may not be completely reliable, and may not allow the original data to be completely reproduced; however, they can greatly compress the data and often give insight into the underlying structure of the data.

The Fril data browser can form rules which predict the unknown values in a database from the known values in that particular case, plus the values in other similar cases. The database is partitioned into fuzzy subsets containing similar values of the variable under consideration; each rule uses fuzzy sets to summarise values of other variables in that partition. These rules can be inspected and understood easily by humans, and can be adjusted in the light of expert knowledge.

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Authors and Affiliations

  1. Advanced Computing Research Centre Department of Engineering Mathematics Queens Building, University Walk, BS8 1TR, Bristol, UK

    J. F. Baldwin & T. P. Martin

Authors
  1. J. F. Baldwin
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  2. T. P. Martin
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Editor information

Trevor P. Martin Anca L. Ralescu

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© 1997 Springer-Verlag Berlin Heidelberg

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Baldwin, J.F., Martin, T.P. (1997). Extracting knowledge from data using an intelligent fuzzy data browser. In: Martin, T.P., Ralescu, A.L. (eds) Fuzzy Logic in Artificial Intelligence Towards Intelligent Systems. FLAI 1995. Lecture Notes in Computer Science, vol 1188. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-62474-0_7

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  • DOI: https://doi.org/10.1007/3-540-62474-0_7

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-62474-5

  • Online ISBN: 978-3-540-49732-5

  • eBook Packages: Springer Book Archive

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