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Tools for Aggregating, Analyzing and Mining Combinatorial Data

Published online by Cambridge University Press:  31 January 2011

Wesley Jones ,
Changwon Suh ,
Peter A Graf ,
Daniel Korytina ,
Craig Swank  and
Christopher Perkins
Wesley Jones
Affiliation:
wesley.jones@nrel.govwesleybjones@me.com, National Renewable Energy Laboratory, Scientific Computing, Golden, Colorado, United States
Changwon Suh
Affiliation:
changwon.suh@nrel.gov, National Renewable Energy Laboratory, Scientific Computing, Golden, Colorado, United States
Peter A Graf
Affiliation:
peter.graf@nrel.gov, National Renewable Energy Laboratory, Scientific Computing, Golden, Colorado, United States
Daniel Korytina
Affiliation:
Daniel.Korytina@nrel.gov, University of Colorado at Boulder, Computer Science, Boulder, Colorado, United States
Craig Swank
Affiliation:
Craig.Swank@nrel.gov, National Renewable Energy Laboratory, Scientific Computing, Golden, Colorado, United States
Christopher Perkins
Affiliation:
Chris.Perkins@nrel.gov, National Renewable Energy Laboratory, Scientific Computing, Golden, Colorado, United States
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Abstract

We demonstrate how data mining techniques can be applied to complex combinatorial data sets and how data from multiple sources can be aggregated via the developed scientific data management system. An example is shown for the case of aggregated combinatorial data for the study of composition, processing, structure, and property relationships of transparent conducting oxides by applying data mining techniques such as principal component analysis. Data mappings of mined results are shown to effectively enable visualization of data trends, identification of anomalies in Fourier transform infrared spectroscopy patterns, and scientifically interesting libraries and spectral regions.

Keywords

transparent conductorcombinatorial synthesisoxide

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