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Data exploration systems for databasesData exploration systems apply machine learning techniques, multivariate statistical methods, information theory, and database theory to databases to identify significant relationships among the data and summarize information. The result of applying data exploration systems should be a better understanding of the structure of the data and a perspective of the data enabling an analyst to form hypotheses for interpreting the data. This paper argues that data exploration systems need a minimum amount of domain knowledge to guide both the statistical strategy and the interpretation of the resulting patterns discovered by these systems.
Document ID
19920014131
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Greene, Richard J.
(Argonne National Lab. IL, United States)
Hield, Christopher
(Argonne National Lab. IL, United States)
Date Acquired
September 6, 2013
Publication Date
January 1, 1992
Publication Information
Publication: NASA. Goddard Space Flight Center, The 1992 Goddard Conference on Space Applications of Artificial Intelligence
Subject Category
Documentation And Information Science
Accession Number
92N23374
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.
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