The federation of heterogeneous large astronomical databases foreseen in the framework of the AVO and NVO projects will pose unprecedented data mining and visualization problems which may find a rather natural and user friendly answer in artificial intelligence (A.I.) tools based on neural networks, fuzzy-C sets or genetic algorithms. We shortly describe some tools implemented by the AstroNeural collaboration (Napoli-Salerno) aimed to perform complex tasks such as, for instance, unsupervised and supervised clustering and time series analysis. Two very different applications to the analysis of photometric redshifts of galaxies in the Sloan Early Data Release and to the telemetry of the TNG (telescopio nazionale Galileo) are also discussed as template cases.
Artificial Intelligence Tools for Data Mining in Large Astronomical Databases / Longo, Giuseppe; C., Donalek; G., Raiconi; A., Staiano; R., Tagliaferri; F., Pasian; Sessa, Salvatore; R., Smareglia. - STAMPA. - (2004), pp. 202-213. [10.1007/10857598_31]
Artificial Intelligence Tools for Data Mining in Large Astronomical Databases
LONGO, GIUSEPPE;SESSA, SALVATORE;
2004
Abstract
The federation of heterogeneous large astronomical databases foreseen in the framework of the AVO and NVO projects will pose unprecedented data mining and visualization problems which may find a rather natural and user friendly answer in artificial intelligence (A.I.) tools based on neural networks, fuzzy-C sets or genetic algorithms. We shortly describe some tools implemented by the AstroNeural collaboration (Napoli-Salerno) aimed to perform complex tasks such as, for instance, unsupervised and supervised clustering and time series analysis. Two very different applications to the analysis of photometric redshifts of galaxies in the Sloan Early Data Release and to the telemetry of the TNG (telescopio nazionale Galileo) are also discussed as template cases.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
