Invention Application
- Patent Title: ITERATIVE VISUALIZATION OF A COHORT FOR WEIGHTED HIGH-DIMENSIONAL CATEGORICAL DATA
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Application No.: US15545895Application Date: 2015-02-20
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Publication No.: US20180004803A1Publication Date: 2018-01-04
- Inventor: Ming C. Hao , Michael Hund , Wei-Nchih Lee , Nelson L. Chang , Daniel Keim , Kevin Smathers , Jishang Wei
- Applicant: Hewlett-Packard Development Company, L.P.
- International Application: PCT/US2015/016893 WO 20150220
- Main IPC: G06F17/30
- IPC: G06F17/30 ; G06F19/00 ; G06Q30/02

Abstract:
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.
Information query