Capacitated spatial clustering with multiple constraints and attributes
Lähderanta, Tero; Lovén, Lauri; Ruha, Leena; Leppänen, Teemu; Launonen, Ilkka; Riekki, Jukka; Sillanpää, Mikko J. (2024)
Lähderanta, Tero
Lovén, Lauri
Ruha, Leena
Leppänen, Teemu
Launonen, Ilkka
Riekki, Jukka
Sillanpää, Mikko J.
Elsevier BV
2024
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe20231013140061
https://urn.fi/URN:NBN:fi-fe20231013140061
Tiivistelmä
Capacitated spatial clustering, a type of unsupervised machine learning method, is often used to tackle problems in compressing data, classification, logistic optimization and infrastructure optimization. Depending on the application at hand, a multitude of extensions to the clustering problem may be necessary. In this article, we propose a number of novel extensions to PACK, a recent capacitated partitional spatial clustering method which uses an optimization algorithm that is based on linear programming tasks. These extensions relate to the relocation and location preference of cluster centers, outliers, and non-spatial attributes, and they can be considered jointly. In the context of edge server placement, these improve the spatial location of servers while considering, for example, application placement on the servers in response to spatial application usage patterns. We demonstrate the usefulness of an extended version of PACK with an example with simulated data, as well as a real world example in edge server placement for a city region with various different setups. These setups are evaluated with summary statistics about spatial proximity and attribute similarity. As a result, the similarity of the clusters was improved by 53% at best while simultaneously the proximity degraded only by 18%. The extensions provide valuable means for including non-spatial information in the cluster analysis, and to attain better overall proximity and similarity.