Astronomy is undergoing through a methodological revolution triggered by an unprecedented wealth of complex and accurate data. DAMEWARE (DAta Mining & Exploration Web Application and REsource) is a general purpose, Web-based, Virtual Observatory compliant, distributed data mining framework specialized in massive data sets exploration with machine learning methods. We present the DAMEWARE (DAta Mining & Exploration Web Application REsource) which allows the scientific community to perform data mining and exploratory experiments on massive data sets, by using a simple web browser. DAMEWARE offers several tools which can be seen as working environments where to choose data analysis functionalities such as clustering, classification, regression, feature extraction etc., together with models and algorithms.

DAMEWARE - Data Mining & Exploration Web Application Resource / Brescia, M.; Cavuoti, S.; Esposito, F.; Fiore, M.; Garofalo, M.; Guglielmo, M.; Longo, G.; Manna, F.; Nocella, A.; Vellucci, C.. - (2016).

DAMEWARE - Data Mining & Exploration Web Application Resource

M. Brescia
Supervision
;
G. Longo;
2016

Abstract

Astronomy is undergoing through a methodological revolution triggered by an unprecedented wealth of complex and accurate data. DAMEWARE (DAta Mining & Exploration Web Application and REsource) is a general purpose, Web-based, Virtual Observatory compliant, distributed data mining framework specialized in massive data sets exploration with machine learning methods. We present the DAMEWARE (DAta Mining & Exploration Web Application REsource) which allows the scientific community to perform data mining and exploratory experiments on massive data sets, by using a simple web browser. DAMEWARE offers several tools which can be seen as working environments where to choose data analysis functionalities such as clustering, classification, regression, feature extraction etc., together with models and algorithms.
2016
DAMEWARE - Data Mining & Exploration Web Application Resource / Brescia, M.; Cavuoti, S.; Esposito, F.; Fiore, M.; Garofalo, M.; Guglielmo, M.; Longo, G.; Manna, F.; Nocella, A.; Vellucci, C.. - (2016).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/900838
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