Technique for fast and efficient hierarchical clustering

DWPI Title: Computer-implemented method for hierarchical clustering of samples in dataset, involves generating nearest neighbor matrix to identify nearest neighbor pairs between samples based on differences between variables of samples
Abstract: A fast and efficient technique for hierarchical clustering of samples in a dataset includes compressing the dataset to reduce a number of variables within each of the samples of the dataset. A nearest neighbor matrix is generated to identify nearest neighbor pairs between the samples based on differences between the variables of the samples. The samples are arranged into a hierarchy that groups the samples based on the nearest neighbor matrix. The hierarchy is rendered to a display to graphically illustrate similarities or differences between the samples.
Use: Computer-implemented method for hierarchical clustering of samples in dataset.
Advantage: The memory and time consumed to generate nearest neighbor matrix can be reduced, since nearest neighbor matrix is generated to identify nearest neighbor pairs between the samples. Hence, samples can be clustered efficiently in dataset.
Novelty: The method involves compressing (210) the dataset to reduce a number of variables within each of the samples of the dataset. A nearest neighbor matrix is generated (215) to identify nearest neighbor pairs between the samples based on differences between the variables of the samples. The samples are arranged into a hierarchy to group the samples based on the nearest neighbor matrix. The hierarchy is rendered (250) to display to graphically illustrate similarities or difference between samples.
Filed: 12/14/2009
Application Number: US2009636898A
Tech ID: SD 11129.0
This invention was made with Government support under Contract No. DE-NA0003525 awarded by the United States Department of Energy/National Nuclear Security Administration. The Government has certain rights in the invention.
Data from Derwent World Patents Index, provided by Clarivate
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