Hostname: page-component-78c5997874-ndw9j Total loading time: 0 Render date: 2024-11-13T02:17:24.242Z Has data issue: false hasContentIssue false

Tools for Aggregating, Analyzing and Mining Combinatorial Data

Published online by Cambridge University Press:  31 January 2011

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
Get access

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.

Type
Research Article
Copyright
Copyright © Materials Research Society 2009

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1 Lewis, A.C., Suh, C., Stukowski, M., Geltmacher, A.B., Rajan, K., and Spanos, G., Scripta Mater. 58, 575 (2008).Google Scholar
2 Sieg, S.C., Suh, C., Schmidt, T., Stukowski, M., Rajan, K., and Maier, W.F., QSAR & Comb. Sci. 26, 528 (2007).Google Scholar
3http://limsfinder.comGoogle Scholar
4http://matdl.orgGoogle Scholar
5 Perkins, J.D., del, J.A. Cueto, Alleman, J.L., Warmsingh, C., Keyes, B.M., Gedvilas, L.M., Parilla, P.A., To, B., Readey, D.W., and Ginley, D.S., Thin Solid Films 411, 152 (2002).Google Scholar
6 Suh, C., Rajan, K., Vogel, B.M., Narasimhan, B., and Mallapragada, S.K., “Informatics Methods for Combinatorial Materials Science,” Combinatorial Materials Science, ed. Narasimhan, B., Mapllapragada, S.K., and Porter, M.D. (Wiley-Interscience, 2007) pp.109119.Google Scholar
7 Wichern, D. and Johnson, R.A., Applied Multivariate Statistical Analysis, 5th ed. (Prentice-Hall, Englewood Cliffs, 2002).Google Scholar
8 Eriksson, L., Johansson, E., Kettaneh-Wold, N., and Wold, S., Multi- and Megavariate Data Analysis / Principles and Applications (Umetrics Academy, Umetrics AB, Umea, Sweden, 2001).Google Scholar
9 Korytina, D., Graf, P.A., King, R., and Jones, W.B., Proc. 2008 International Conference on Information & Knowledge Engineering. IKE 2008, 111(2008).Google Scholar
10 Kimball, R. and Ross, M., The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling, 2nd Ed. (John Wiley & Sons, New York, 2002).Google Scholar
11 Perkins, J.D., Teplin, C.W., van Hest, M.F.A.M, Alleman, J.L., Li, X., Dabney, M.S., Keyes, B.M., Gedvilas, L.M., Ginley, D.S., Lin, Y., and Lu, Y., Appl. Surf. Sci. 223, 124 (2004).Google Scholar
12 Ludäscher, B., Altintas, I., Berkley, C., Higgins, D., Jaeger, E., Jones, M., Lee, E.A., Tao, J., and Zhao, Y., Concurr Comp-Pranct E 18, 1039 (2005).Google Scholar
13 Williams, R., Bunn, J., Moore, R., and Pool, J., Eds., Interfaces to Scientific Data Archives, California Institute of Technology, 1 (1998).Google Scholar