NDLI: Text data mining applied to clustering with cost effective tools

Content Provider	IEEE Xplore Digital Library
Author	Moebes, T.A.
Copyright Year	2005
Description	Author affiliation: Safety & Mission Assurance, Sci. Applications Int. Corp., Houston, TX, USA (Moebes, T.A.)
Abstract	Classification by reading of corrective action reports at NASA can be a lengthy and labor intensive process. This paper shows that a process requiring several weeks of engineer labor can be reduced to a few hours of analyst labor using commercial and in-house data mining applications. Signal processing theory is used to determine the best cluster based on text to use when searching for common cause problems. A method of determining the high-level clusters is presented, and this is followed by a new technique using Fourier transformations and cross-correlations to determine more refined low-level clusters and new information in the data. Finally, a way to apply these results to situations where the cost of lengthy decisions is different from the rewards for quick, correct decisions is discussed. By developing special in-house software, much of the text data mining can be accomplished without purchasing expensive specialized text mining tools.
Starting Page	2787
Ending Page	2794
File Size	280032
Page Count	8
File Format	PDF
ISBN	0780392981
DOI	10.1109/ICSMC.2005.1571572
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-10-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data mining Costs Data analysis NASA Failure analysis Performance analysis Aerospace safety Data engineering Signal processing Software tools linear regression Fast Fourier Transformation cross-correlation probability statistics
Content Type	Text
Resource Type	Article

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