Comparative Analysis of Clustering Techniques for Personalized Food Kit Distribution

TL;DR

This study compares clustering techniques to personalize food kit distribution, aiming to improve supply relevance based on consumer preferences using real-world data and various clustering methods.

Contribution

It provides a comparative analysis of clustering methods for personalized food kit distribution, including a novel reassignment enhancement based on cluster loss thresholds.

Findings

Centroid-based clustering methods outperform others in this context.

Reassignment based on cluster loss improves clustering efficacy.

Identifies the most effective clustering technique for personalized food kit design.

Abstract

The Government of Kerala had increased the frequency of supply of free food kits owing to the pandemic, however, these items were static and not indicative of the personal preferences of the consumers. This paper conducts a comparative analysis of various clustering techniques on a scaled-down version of a real-world dataset obtained through a conjoint analysis-based survey. Clustering carried out by centroid-based methods such as k means is analyzed and the results are plotted along with SVD, and finally, a conclusion is reached as to which among the two is better. Once the clusters have been formulated, commodities are also decided upon for each cluster. Also, clustering is further enhanced by reassignment, based on a specific cluster loss threshold. Thus, the most efficacious clustering technique for designing a food kit tailored to the needs of individuals is finally obtained.