Nested Pattern Detection and Unidimensional Process Characterization
Year: 2024
Type: article
Abstract: This document introduces methods for describing long texts as groups of repeating symbols or patterns. The process converts a series of real-number values into texts. Developed tailored algorithms for identifying repeated sequences in the text are applied to decompose the text into nested tree-like structures of repeating symbols and is called the Nested Repeated Sequence Decomposition Model (NRSDM). The NRSDM is especially valuable for extracting repetitive behaviors in oscillatory but non-periodic and chaotic processes where the classical Fourier transform has limited application. The NRSDM along with the two graphical representations proposed here form a promising tool for characterizing long texts configured to represent the behavior of unidimensional processes.
Source: Entropy
DOI: 10.3390/e26090754
Author: Gerardo L. Febres
Institution: Universidad Simón Bolívar
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,955
OpenAlex ID: W4402207121