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This software is an automatic n-dimensional quantitative data clustering tool based on five optimization heuristics (GA, PSO, ACO, SA, and TS). It enables performance comparisons across heuristics and parameter testing within each method, analyzing solution quality, runtime, and convergence; it also includes a repository of benchmark test tables and an intuitive interface for research and operational use.
It was developed by M.Sc. Jeffry Chavarría-Molina and M.Sc. Juan José Fallas-Monge at the Instituto Tecnológico de Costa Rica (TEC), School of Mathematics, with support from the Vice-Rectory for Research and Extension (VIE). The software was a direct outcome of “Combinatorial optimization heuristics for data classification” (Code: 5402-1440-3901), and its development continued within the related project “Disease propagation: heuristics applied to optimizing control measures” (Code: 540114404201).
Features
- N-dimensional data clustering based on five advanced metaheuristics
- Performance comparison across heuristics, enabling the evaluation of each method’s efficiency and accuracy in data clustering.
- Parameter analysis for each heuristic, enabling the study of how parameter variation influences performance, execution time, and convergence for each method.
- An intuitive, user-friendly interface designed to make the software easy to use without requiring advanced knowledge of heuristics.
- A repository of benchmark test tables, providing predefined datasets that allow you to evaluate each heuristic’s performance across different scenarios.
- It allows you to easily export results to Excel files, making it quick, clear, and convenient to analyze the resulting clustering.
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