Conference article

Flat Patterns Extraction with Collinearity Models

Leon Bobrowski
Faculty of Computer Science, Bialystok University of Technology, Bialystok, Poland / Institute of Biocybernetics and Biomedical Engineering, PAS, Warsaw, Poland

Pawel Zabielski
Institute of Biocybernetics and Biomedical Engineering, PAS, Warsaw, Poland

Download articlehttp://dx.doi.org/10.3384/ecp17142518

Published in: Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016

Linköping Electronic Conference Proceedings 142:75, p. 518-524

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Published: 2018-12-19

ISBN: 978-91-7685-399-3

ISSN: 1650-3686 (print), 1650-3740 (online)

Abstract

The term collinear (flat) pattern means in this article, a set of a large number of feature vectors located on (or near) a plane in multidimensional feature space. Flat patterns extracted from large data set can provide a basis for modeling a local interactions in selected sets of features. Collinear patterns can be discovered in given data set through minimization of some kind of the convex and piecewise linear (CPL) criterion functions.

Keywords

data mining, flat patterns, CPL criterion functions, margins

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