TY - JOUR

T1 - Path integral distance for the automated data interpretation

AU - Volchenkov, D.

N1 - Publisher Copyright:
© 2014 L & H Scientific Publishing, LLC.

PY - 2014

Y1 - 2014

N2 - The process of data interpretation is always based on the implicit introduction of equivalence relations on the set of walks over the database. Every equivalence relation on the set of walks specifies a Markov chain describing the transitions of a discrete time random walk. In order to geometrize and interpret the data, we propose the new distance between data units defined as a "Feynman path integral", in which all possible paths between any two nodes in a graph model of the data are taken into account, although some paths are more preferable than others. Such a path integral distance approach to the analysis of databases has proven its efficiency and success, especially on multivariate strongly correlated data where other methods fail to detect structural components (urban planning, historical language phylogenies, music, street fashion traits analysis, etc.). We believe that it would become an invaluable tool for the intelligent complexity reduction and big data interpretation.

AB - The process of data interpretation is always based on the implicit introduction of equivalence relations on the set of walks over the database. Every equivalence relation on the set of walks specifies a Markov chain describing the transitions of a discrete time random walk. In order to geometrize and interpret the data, we propose the new distance between data units defined as a "Feynman path integral", in which all possible paths between any two nodes in a graph model of the data are taken into account, although some paths are more preferable than others. Such a path integral distance approach to the analysis of databases has proven its efficiency and success, especially on multivariate strongly correlated data where other methods fail to detect structural components (urban planning, historical language phylogenies, music, street fashion traits analysis, etc.). We believe that it would become an invaluable tool for the intelligent complexity reduction and big data interpretation.

KW - Data interpretation Markov chains

KW - Multivariate strongly correlated data

KW - Path integral distance

UR - http://www.scopus.com/inward/record.url?scp=85020282942&partnerID=8YFLogxK

U2 - 10.5890/DNC.2014.09.004

DO - 10.5890/DNC.2014.09.004

M3 - Article

AN - SCOPUS:85020282942

VL - 3

SP - 255

EP - 279

JO - Discontinuity, Nonlinearity, and Complexity

JF - Discontinuity, Nonlinearity, and Complexity

SN - 2164-6376

IS - 3

ER -