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Supplementary Materials for 'New Cluster Selection and Fine-grained Search for k-Means Clustering and Wi-Fi Fingerprinting'

Authors: Torres-Sospedra, Joaquín; Quezada-Gaibor, Darwin; Mendoza-Silva, Germán; Nurmi, Jari; Koucheryavy, Yevgeny; Huerta, Joaquín;

Supplementary Materials for 'New Cluster Selection and Fine-grained Search for k-Means Clustering and Wi-Fi Fingerprinting'

Abstract

Supplementary Materials for "New Cluster Selection and Fine-grained Search for k-Means Clustering and Wi-Fi Fingerprinting https://zenodo.org/record/3751042 This package includes the materials and methods to reproduce the results presented in the paper detailed below. If you would like to re-use the software provided and/or the empirical evaluation please cite the two following items. Torres-Sospedra, J.; Quezada-Gaibor, D.; Mendoza-Silva, G. M.; Nurmi, J.; Koucheryavy, Y. and Huerta, J. "New Cluster Selection and Fine-grained Search for k-Means Clustering and Wi-Fi Fingerprinting". In Proceedings of the Tenth International Conference on Localization and GNSS (ICL-GNSS), 2020. Torres-Sospedra, J.; Quezada-Gaibor, D.; Mendoza-Silva, G. M.; Nurmi, J.; Koucheryavy, Y. and Huerta, J. "Supplementary Materials for 'New Cluster Selection and Fine-grained Search for k-Means Clustering and Wi-Fi Fingerprinting'", Zenodo, 2020. [Available Online] https://zenodo.org/record/3751042 If you would like to re-use the databases included in this paper, please cite the corresponding sources as indicated in the readme file in the folder 'databases'. If you want to re-run the experiments in your computer, please execute the script executeAll (provided for MatLab/Octave and linux shell). A summary of the results has been included to generate the figures and tables included in the paper. If you want to generate the same figures for the results obtained with your computer, please execute the script csv4all to generate the new summarized results in the "Results_CSV" folder and modify the scripts included in folder "Tables_and_Figures" to target the new summarized. If you have any questions about this package, please do not hesitate to contact J. Torres-Sospedra (jtorres@uji.es -- info@jtorr.es -- jtorres.phd@gmail.com).

This package includes the function localclustering_kmeans.m which was originally included in Octave 5.1.0 Statistical Package as kmeans.m. It was developed by Soren Hauberg (2011), Daniel Ward (2012) and Lachlan Andrew (2015-2016); and released under GNU General Public License. The authors gratefully acknowledge funding from Ministerio de Ciencia, Innovación y Universidades (INSIGNIA, PTQ2018-009981); European Union's H2020 Research and Innovation programme under the Marie Skłodowska-Curie grant agreement No.813278 (A-WEAR, http://www.a-wear.eu/); and Universitat Jaume I (PREDOC/2016/55).}

{"references": ["J. Torres-Sospedra, D. Quezada-Gaibor, G. M. Mendoza-Silva, J. Nurmi, Y. Koucheryavy and J. Huerta, \"New Cluster Selection and Fine-grained Search for k-Means Clustering and Wi-Fi Fingerprinting,\" 2020 International Conference on Localization and GNSS (ICL-GNSS), Tampere, Finland, 2020, pp. 1-6, doi: 10.1109/ICL-GNSS49876.2020.9115419."]}

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Keywords

k-Means, Indoor Positioning, k-NN, Clustering

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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