Open AccessA Strict Total Order and Derived Distance Function for Multivariate Finite Data Based on Data-derived Analytical Meshes
Author(s) -
Ray-Ming Chen
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1789/1/012012
Subject(s) - polygon mesh , multivariate statistics , set (abstract data type) , linearization , function (biology) , mathematics , data set , simple (philosophy) , algorithm , finite set , computer science , mathematical optimization , nonlinear system , mathematical analysis , statistics , geometry , philosophy , physics , epistemology , quantum mechanics , evolutionary biology , biology , programming language
In this article, we show a way to linearize a set of finite multivariate data set. Such linearization utilizes analytical meshes, which are defined based on a given data, and associated forward walking paths related to the defined meshes. It further yields a strict linear ordering and a simple distance function via such walking paths and lengths for the given data set. This strict linear ordering and distance function could be applied in grouping the data set with respect to some optimal criteria.