Hierarchical clustering using correlation metric and spatial continuity constraint

DWPI Title: Computer-implemented method for processing data set, involves populating data structure with multiple entries that correspond to respective ones of data vectors, and correlation calculations are performed to determine every pair of entries
Abstract: Large data sets are analyzed by hierarchical clustering using correlation as a similarity measure. This provides results that are superior to those obtained using a Euclidean distance similarity measure. A spatial continuity constraint may be applied in hierarchical clustering analysis of images.
Use: Computer-implemented method for processing data set.
Advantage: The correlation values are used to identify a pair of entries that are provided with an associated pair of data vectors, and an identified pair of entries is recorded in another data structure, and thus ensures fast and computationally efficient hierarchical clustering techniques of the data set processing method.
Novelty: The computer-implemented method involves populating a data structure with multiple entries that correspond to respective ones of the data vectors. The correlation calculations are performed to determine every pair of entries in the data structure. The correlation values are used to identify a pair of entries that are provided with an associated pair of data vectors, and an identified pair of entries is recorded in another data structure. The pair of data vectors associated with the identified pair of entries is merged to define a merged data vector.
Filed: 3/7/2011
Application Number: US13042053A
Tech ID: SD 11479.0
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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