2026-06-03T05:26:52Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/163882026-02-03T12:01:18Zcom_10630_2254col_10630_37959

Data mining for municipal financial distress prediction Alaminos Aguilera, David Fernández, David García-Lopera, Francisca Fernández, Manuel Ángel Minería de datos (Informática) Data mining techniques are capable of extracting valuable knowledge from large and variable databases. This work proposes a data mining method for municipal financial distress prediction. Using a new proxy of municipal financial situation and a sample of 128 Spanish municipalities, the empirical experiment obtained satisfactory results, which testifies to the viability and validity of the data mining method proposed for municipal financial distress prediction. 2018-07-31T10:20:36Z 2018-07-31T10:20:36Z 2018-07-31T10:20:36Z 2018-07-31 conference output https://hdl.handle.net/10630/16388 eng 18th Industrial Conference on Data Mining ICDM 2018 Nueva York. USA 11-15 de Julio open access