Abstract
In an ever-developing world with growing population and increasing demand for natural re-sources, sustainability evaluation becomes more relevant in order to inform decision makers ade-quately. Data mining presents itself as a useful tool to extract information from vast arrays of data and its relevance when dealing with sustainability indexes can be maximized when together with multicriteria decision aid techniques. In this research, data from the SSF Index is clustered using: 1- k-means application on the aggregated information and 2- an approach utilizing k-means fol-lowed by ELECTRE-III ranking method over all the indicator set. Compared to the original data stratification, the proposed method creates groups that are more similar in terms of subsets of indicators, enriching the knowledge provided by the index, while not presenting the shortcoming of loss of information caused by aggregation.
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de Souza, R.L.S., Costa, H.G. (2021). K-Means and Multicriteria Decision Aid Applied to Sustainability Evaluation. In: Abraham, A., Piuri, V., Gandhi, N., Siarry, P., Kaklauskas, A., Madureira, A. (eds) Intelligent Systems Design and Applications. ISDA 2020. Advances in Intelligent Systems and Computing, vol 1351. Springer, Cham. https://doi.org/10.1007/978-3-030-71187-0_111
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