Authors:
Diego Vallejo-Huanga
1
;
2
;
Cèsar Ferri
2
and
Fernando Martínez-Plumed
2
Affiliations:
1
IDEIAGEOCA Research Group, Universidad Politécnica Salesiana, Quito, Ecuador
;
2
VRAIN, Universitat Politècnica de València, Valencia, Spain
Keyword(s):
Size-Constrained Clustering, K-MedoidsSC, CSCLP, Interactive Web Application, R Shiny, User Experience.
Abstract:
Size-constrained clustering addresses a fundamental need in many real-world applications by ensuring that clusters adhere to user-specified size limits, whether to balance groups or to satisfy domain-specific requirements. In this paper, we present ClustSize, an interactive web platform that implements two advanced algorithms: K-MedoidsSC and CSCLP, to perform real-time clustering of tabular data under strict size constraints. Developed in R Studio using the Shiny framework and deployed on Shinyapps.io, ClustSize not only enforces precise cluster cardinalities, but also facilitates dynamic parameter tuning and visualisation for enhanced user exploration. We comprehensive validate its performance through comprehensive benchmarking, also evaluating runtime, RAM usage, load, and stress conditions, and gather usability insights via user surveys. Post-deployment evaluations confirm that both algorithms consistently produce clusters that exactly meet the specified size limits, and that the
system reliably supports up to 50 concurrent users and maintains functionality under stress, processing approximately 90 requests in 5 seconds. These results highlight the potential of integrating advanced size-constrained clustering into interactive web platforms for practical data analysis.
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