We explore attribute dependencies in the datasets by using direct and inverse fuzzy transforms. Our algorithm optimizes the fuzzy partitions of the universe of the attributes and moreover establishes if the set of the data points is sufficiently dense with respect to the chosen partitions: two specific regression indexes measure the reliability of our model. The known “El Nino” dataset is the basis of our experiments, whose results are consistent with the regression analysis made with the same data.
Multi-dimensional fuzzy transforms for attribute dependencies / Di Martino, F.; Loia, V.; Sessa, Salvatore. - STAMPA. - (2009), pp. 53-57. (Intervento presentato al convegno IFSA-EUSFLAT 2009 tenutosi a lisbona nel 20-24 luglio 2009).
Multi-dimensional fuzzy transforms for attribute dependencies
F. Di Martino;SESSA, SALVATORE
2009
Abstract
We explore attribute dependencies in the datasets by using direct and inverse fuzzy transforms. Our algorithm optimizes the fuzzy partitions of the universe of the attributes and moreover establishes if the set of the data points is sufficiently dense with respect to the chosen partitions: two specific regression indexes measure the reliability of our model. The known “El Nino” dataset is the basis of our experiments, whose results are consistent with the regression analysis made with the same data.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
