Streamlining a Meteorological Database for Knowledge Discovery

C. M. Howard; V. J. Rayward-Smith

doi:10.1049/ic:19971154

Streamlining a Meteorological Database for Knowledge Discovery

C. M. Howard, V. J. Rayward-Smith

School of Computing Sciences

Research output: Contribution to conference › Other

1 Citation (Scopus)

Abstract

Knowledge discovery in databases (KDD) is a multi stage process for extracting non trivial information from databases; two such stages are data preparation and data mining. We show how KDD techniques have been applied to a meteorological database to search for climatic patterns between geographical locations and how the size and quality of the data give rise to problems in the data mining stage.

Original language	English
Pages	9/1-9/5
DOIs	https://doi.org/10.1049/ic:19971154
Publication status	Published - Dec 1997
Event	IEE Colloquium on IT Strategies for Information Overload - London, United Kingdom Duration: 3 Dec 1997 → …

Conference

Conference	IEE Colloquium on IT Strategies for Information Overload
Country/Territory	United Kingdom
City	London
Period	3/12/97 → …

Access to Document

10.1049/ic:19971154

Cite this

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title = "Streamlining a Meteorological Database for Knowledge Discovery",

abstract = "Knowledge discovery in databases (KDD) is a multi stage process for extracting non trivial information from databases; two such stages are data preparation and data mining. We show how KDD techniques have been applied to a meteorological database to search for climatic patterns between geographical locations and how the size and quality of the data give rise to problems in the data mining stage.",

author = "Howard, {C. M.} and Rayward-Smith, {V. J.}",

year = "1997",

month = dec,

doi = "10.1049/ic:19971154",

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note = "IEE Colloquium on IT Strategies for Information Overload ; Conference date: 03-12-1997",

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AU - Howard, C. M.

AU - Rayward-Smith, V. J.

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AB - Knowledge discovery in databases (KDD) is a multi stage process for extracting non trivial information from databases; two such stages are data preparation and data mining. We show how KDD techniques have been applied to a meteorological database to search for climatic patterns between geographical locations and how the size and quality of the data give rise to problems in the data mining stage.

U2 - 10.1049/ic:19971154

DO - 10.1049/ic:19971154

M3 - Other

SP - 9/1-9/5

T2 - IEE Colloquium on IT Strategies for Information Overload

Y2 - 3 December 1997

ER -