IJPAM: Volume 51, No. 2 (2009)

Invited Lecture Delivered at
Fifth International Conference of Applied Mathematics
and Computing (Plovdiv, Bulgaria, August 12-18, 2008)


Dmitriy Khots$^1$, Boris Khots$^2$
$^1$3710, S. 202-nd Avenue, Omaha, NE 68130, USA
e-mail: [email protected]
$^2$Compressor Controls Corporation
4725, 121-st Street, Des Moines, IA 50323-2316, USA
e-mail: [email protected]

Abstract.This work considers Data Mining aspects in a setting of arithmetic provided by Observer's Mathematics (see www.mathrelativity.com). We prove that Data Mining methods based on Observer's Mathematics are more robust than classical methods for certain feature spaces. We further present applications of Observer's Mathematics to data mining problems in Physics and Genetics. Certain theoretical results and communications pertaining to these theorems are also provided.

Received: August 14, 2008

AMS Subject Classification: 03B30

Key Words and Phrases: arithmetic, data mining, physics

Source: International Journal of Pure and Applied Mathematics
ISSN: 1311-8080
Year: 2009
Volume: 51
Issue: 2