Iterative visualization of a cohort for weighted high-dimensional categorical data

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United States of America Patent

PATENT NO 10628412
SERIAL NO

15545895

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Abstract

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Visualization of a cohort for high-dimensional categorical data is disclosed. One example is a system including a display module to identify real-time selection of a query data element in an interactive visual representation of high-dimensional categorical data elements comprising a plurality of categorical components. A matrix generator generates a binary distance matrix with columns representing categorical components, and entries in a row indicative of a degree of similarity of respective categorical components of the selected query data element to a data element represented by the row, and determines a category weighting matrix by associating a weight with entries in each column of the binary distance matrix. An evaluator evaluates a weighted similarity score for a data element represented by a row of the category weighting matrix based on entries of the row. A selector iteratively and interactively selects, based on weighted similarity scores, a cohort of categorical data elements.

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Patent Owner(s)

Patent OwnerAddress
HEWLETT-PACKARD DEVELOPMENT COMPANY L P10300 ENERGY DRIVE SPRING TX 77389

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Inventor(s)

Inventor Name Address # of filed Patents Total Citations
Chang, Nelson L San Jose, US 32 213
Hao, Ming C Palo Alto, US 100 2535
Hund, Michael Constance, DE 12 34
Keim, Daniel Constance, DE 39 272
Lee, Wei-Nchih Palo Alto, US 14 40
Smathers, Kevin Hayward, US 22 87
Wei, Jishang Palo Alto, US 19 64

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