Method and apparatus for clustering and visualization of...

Data processing: measuring – calibrating – or testing – Measurement system – Statistical measurement

Reexamination Certificate

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C702S021000, C702S190000

Reexamination Certificate

active

07853432

ABSTRACT:
A computer implemented method is provided for clustering and visualization of an n-dimensional space in a Euclidean space. The method includes: collecting a plurality of multi-dimensional data sets; estimating a probability density function from each data set; approximating a dissimilarity between every pair of said probability density functions and recording every dissimilarity in a dissimilarity matrix; embedding the dissimilarity matrix into a Euclidean space having a dimensionality of three or less using a multi-dimensional scaling method; and graphically displaying relationships between data sets using data in the Euclidean space on a display of a computing device.

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patent: 2007/0100880 (2007-05-01), Buscema
patent: 0007306 (2005-01-01), None
patent: 02/093810 (2002-11-01), None
Shaohua Kevin Zhou et al., “From Sample Similarity to Ensemble Similarity: Probabilistic Distance Measures in Reproducing Kernel Hilbert Space”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, No. 6, Jun. 2006.
Qing T. Zeng et al., “Feature-guided clustering of multi-dimensional flow cytometry datasets”, ScienceDirect, Journal of Biomedical Informatics 40 (2007) 325-331, available online Jun. 27, 2006.
Sang-Mook Lee et al., “Dimensionality Reduction and Clustering on Statistical Manifolds”, Computer Vision and Pattern Recognition, 2007. CVPR '07. IEEE Conference. Publicataion Date: Jun. 17-22, 2007.

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