Published June 4, 2020 | Version 1.0
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Supplementary Materials for 'New Cluster Selection and Fine-grained Search for k-Means Clustering and Wi-Fi Fingerprinting'

Creators

  • 1. UBIK Geospatial Solutions
  • 2. Institute of New Imaging Technologies, Universitat Jaume I, Castellón, Spain
  • 3. Electrical Engineering Unit, Tampere University, Tampere, Finland

Description

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).

 

Notes

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).}

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Additional details

Funding

A-WEAR – A network for dynamic WEarable Applications with pRivacy constraints 813278
European Commission

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.