Data Mining and Multivariate Analysis in Materials Science

Informatics Strategies for Materials Databases
  • Krishna Rajan
  • A. Rajagopalan
  • C. Suh
Part of the NATO Science Series book series (NAII, volume 52)


Databases in materials science applications tend to be phenomenological in nature. In other words, they are built around a taxonomy of specific classes of properties and materials characteristics. In order for databases to serve as more than only a “search and retrieve” infrastructure, and more for a tool for “knowledge discovery”, data bases need to have functional capabilities. The recent advances in genomics and proteomics for instance provide a good example of the development of such “functional” databases. A first step to achieve this is to develop descriptors of materials properties that can be sorted and classified using appropriate data mining algorithms. In this paper we provide some examples of the use of some well established statistical tools to “prepare” such data especially when there is a multi-dimensional component associated with structure- chemistry-property relationships.


Molten Salt Information Infrastructure Data Mining Algorithm Data Mining Tool Multivariate Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media Dordrecht 2002

Authors and Affiliations

  • Krishna Rajan
    • 1
  • A. Rajagopalan
    • 1
  • C. Suh
    • 1
  1. 1.Department of Materials Science and EngineeringRensselaer Polytechnic InstituteTroyUSA

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