Comparative Analysis of Clustering Techniques for Personalized Food Kit Distribution
This addresses the need for personalized food distribution for consumers in Kerala, but it is incremental as it applies existing clustering methods to a new domain-specific dataset.
The paper tackled the problem of static food kits not reflecting personal preferences by comparing clustering techniques on a real-world dataset to design personalized kits, concluding that k-means with reassignment was more effective than SVD-based methods.
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.