Communications of the ACM
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: concepts and techniques
Data mining: concepts and techniques
Tri-plots: scalable tools for multidimensional data mining
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Continually evaluating similarity-based pattern queries on a streaming time series
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
What Is the Nearest Neighbor in High Dimensional Spaces?
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
XmdvTool: integrating multiple methods for visualizing multivariate data
VIS '94 Proceedings of the conference on Visualization '94
Similarity Search in Multimedia Databases
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Learning semantics-preserving distance metrics for clustering graphical data
MDM '05 Proceedings of the 6th international workshop on Multimedia data mining: mining integrated media and complex data
On the marriage of Lp-norms and edit distance
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
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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.