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Network-Based and Binless Frequency Analyses
We introduce and develop a new network-based and binless methodology to perform frequency analyses and produce histograms. In contrast with traditional frequency analysis techniques that use fixed intervals to bin values, we place a range ±ζ around each individual value in a data set and count the n...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4631440/ https://www.ncbi.nlm.nih.gov/pubmed/26529207 http://dx.doi.org/10.1371/journal.pone.0142108 |
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author | Derrible, Sybil Ahmad, Nasir |
author_facet | Derrible, Sybil Ahmad, Nasir |
author_sort | Derrible, Sybil |
collection | PubMed |
description | We introduce and develop a new network-based and binless methodology to perform frequency analyses and produce histograms. In contrast with traditional frequency analysis techniques that use fixed intervals to bin values, we place a range ±ζ around each individual value in a data set and count the number of values within that range, which allows us to compare every single value of a data set with one another. In essence, the methodology is identical to the construction of a network, where two values are connected if they lie within a given a range (±ζ). The value with the highest degree (i.e., most connections) is therefore assimilated to the mode of the distribution. To select an optimal range, we look at the stability of the proportion of nodes in the largest cluster. The methodology is validated by sampling 12 typical distributions, and it is applied to a number of real-world data sets with both spatial and temporal components. The methodology can be applied to any data set and provides a robust means to uncover meaningful patterns and trends. A free python script and a tutorial are also made available to facilitate the application of the method. |
format | Online Article Text |
id | pubmed-4631440 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-46314402015-11-13 Network-Based and Binless Frequency Analyses Derrible, Sybil Ahmad, Nasir PLoS One Research Article We introduce and develop a new network-based and binless methodology to perform frequency analyses and produce histograms. In contrast with traditional frequency analysis techniques that use fixed intervals to bin values, we place a range ±ζ around each individual value in a data set and count the number of values within that range, which allows us to compare every single value of a data set with one another. In essence, the methodology is identical to the construction of a network, where two values are connected if they lie within a given a range (±ζ). The value with the highest degree (i.e., most connections) is therefore assimilated to the mode of the distribution. To select an optimal range, we look at the stability of the proportion of nodes in the largest cluster. The methodology is validated by sampling 12 typical distributions, and it is applied to a number of real-world data sets with both spatial and temporal components. The methodology can be applied to any data set and provides a robust means to uncover meaningful patterns and trends. A free python script and a tutorial are also made available to facilitate the application of the method. Public Library of Science 2015-11-03 /pmc/articles/PMC4631440/ /pubmed/26529207 http://dx.doi.org/10.1371/journal.pone.0142108 Text en © 2015 Derrible, Ahmad http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Derrible, Sybil Ahmad, Nasir Network-Based and Binless Frequency Analyses |
title | Network-Based and Binless Frequency Analyses |
title_full | Network-Based and Binless Frequency Analyses |
title_fullStr | Network-Based and Binless Frequency Analyses |
title_full_unstemmed | Network-Based and Binless Frequency Analyses |
title_short | Network-Based and Binless Frequency Analyses |
title_sort | network-based and binless frequency analyses |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4631440/ https://www.ncbi.nlm.nih.gov/pubmed/26529207 http://dx.doi.org/10.1371/journal.pone.0142108 |
work_keys_str_mv | AT derriblesybil networkbasedandbinlessfrequencyanalyses AT ahmadnasir networkbasedandbinlessfrequencyanalyses |