Abstract
Scientific datasets often consist of complex data types such as images. Mining such data presents interesting issues related to semantics. In this paper, we explore the research issues in mining data from the field of nanotechnology. More specifically, we focus on a problem that relates to image comparison of material nanostructures. A significant challenge here relates to the notion of similarity between the images. Features such as size and height of nano-particles and inter-particle distance are important in image similarity as conveyed by domain experts. However, there are no precise notions of similarity defined apriori. Hence there is a need for learning similarity measures. In this paper, we describe our proposed approach to learn similarity measures for graphical data. We discuss this with reference to nanostructure images. Other challenges in image comparison are also outlined. The use of this research is discussed with respect to targeted applications.
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Varde, A., Liang, J., Rundensteiner, E., Sisson, R. (2006). Mining Images of Material Nanostructure Data. In: Madria, S.K., Claypool, K.T., Kannan, R., Uppuluri, P., Gore, M.M. (eds) Distributed Computing and Internet Technology. ICDCIT 2006. Lecture Notes in Computer Science, vol 4317. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11951957_35
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DOI: https://doi.org/10.1007/11951957_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68379-7
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