A Robust and Scalable Solution for Interpolative Multidimensional Scaling With Weighting

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Provided by: Indiana University
Topic: Big Data
Format: PDF
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, the authors use Multi-Dimensional Scaling (MDS) to reduce the dimension of original data and Pairwise clustering, and to classify the data. They have shown that an interpolative technique can be applied to get better performance on massive data.
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