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A Robust and Scalable Solution for Interpolative Multidimensional Scaling with Weighting
2013
2013 IEEE 9th International Conference on e-Science
Advances in modern bio-sequencing techniques have led to a proliferation of raw genomic data that enables an unprecedented opportunity for data mining. To analyze such large volume and high-dimensional scientific data, many high performance dimension reduction and clustering algorithms have been developed. Among the known algorithms, we use Multidimensional Scaling (MDS) to reduce the dimension of original data and Pairwise Clustering, and to classify the data. We have shown that an
doi:10.1109/escience.2013.30
dblp:conf/eScience/RuanF13
fatcat:des7ike4sza3pnsdklec2pcrju