Home > Archive > 2020 > Volume 10 Number 1 (Jan. 2020) >
IJMLC 2020 Vol.10(1): 207-212 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2020.10.1.921

Logistic Profile Generation via Clustering Analy

Andres Regal

Abstract—The process of characterizing a city to generate logistic profiles involves the analysis of many different aspects. These profiles are based on secondary sources of data, mainly road network infrastructure, socio-economic data and population density. Following previous research, the final profiles are given by a K-Means algorithm, which uses principal component analysis (PCA) for correlation analysis. A caveat in this method is that prior research has shown that PCA is sensitive to outliers and high dimensionality, which may mislead the following analysis and research. As such, this paper proposes a methodology to evaluate the performance of different clustering techniques to generate logistic profiles, applying it to a case study in the city of Lima, Perú.

Index Terms—Clustering analysis, last mile logistics, logistics, territorial intelligence.

Andres Regal is with Universidad del Pacifico, Lima, Peru (e-mail: a.regalludowieg@up.edu.pe).

[PDF]

Cite: Andres Regal, "Logistic Profile Generation via Clustering Analy," International Journal of Machine Learning and Computing vol. 10, no. 1, pp. 207-212, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

 

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net


Article Metrics in Dimensions