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Twenty-four courses are analyzed across seven key metrics of Coordination for the Improvement of Higher Education Personnel (CAPES): CAPES Scale, Physical Structure, Faculty Quality, Society Impact, Efficiency, Intellectual Production, and Relevance. The robustness of the methodology is validated through KMO (0.64), Kaiser\u2019s, and Bartlett\u2019s sphericity tests ([Formula: see text]-value of 1.66[Formula: see text]), complemented by sensitivity analysis using MCDA methods. The results show high Spearman correlations with CROWM: 0.89 for CoCoSo, 1.00 for PCA, 0.95 for THOR 2, and 0.81 for WISP, demonstrating strong alignment with well-established MCDA methodologies. CROWM\u2019s limitations include dataset scope, generalizability, reliance on PCA validation, subjective input exclusion, and data structure sensitivity. Beyond the Brazilian context, this methodology provides a versatile framework adaptable to international educational systems, corporate training programs, and public sector evaluations, provided that the analyzed data are quantitative and objectively defined. Its structured, data-driven approach ensures reliability in decision-making across various fields, where statistical validation and objective evaluation criteria are essential.<\/jats:p>","DOI":"10.1142\/s0219622025500695","type":"journal-article","created":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T06:30:51Z","timestamp":1751005851000},"page":"825-865","source":"Crossref","is-referenced-by-count":0,"title":["Unsupervised Machine Learning in the Evaluation of Brazilian Postgraduate Programs: Decision Support Via the Crowm Method"],"prefix":"10.1142","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4596-0649","authenticated-orcid":false,"given":"Arthur Pinheiro","family":"de Ara\u00fajo Costa","sequence":"first","affiliation":[{"name":"Systems and Computing, Military Institute of Engineering (IME), Rio de Janeiro RJ 22290-270 - Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6656-1247","authenticated-orcid":false,"given":"Julio Cesar","family":"Duarte","sequence":"additional","affiliation":[{"name":"Systems and Computing, Military Institute of Engineering (IME), Rio de Janeiro RJ 22290-270 - Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1688-0586","authenticated-orcid":false,"given":"Ronaldo Ribeiro","family":"Goldschmidt","sequence":"additional","affiliation":[{"name":"Systems and Computing, Military Institute of Engineering (IME), Rio de Janeiro RJ 22290-270 - Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1533-5535","authenticated-orcid":false,"given":"Marcos dos","family":"Santos","sequence":"additional","affiliation":[{"name":"Systems and Computing, Military Institute of Engineering (IME), Rio de Janeiro RJ 22290-270 - Brazil"}]}],"member":"219","published-online":{"date-parts":[[2025,7,31]]},"reference":[{"key":"S0219622025500695BIB001","volume-title":"Machine Learning: An Artificial Intelligence Approach","author":"Michalski R. 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