Data mining allows for hidden patterns to be brought to light in large data sets. This paper aims to apply data mining on real-life data showing Greenhouse Gas Emissions for countries within the European Union. Greenhouse Gasses are gasses which are released into the atmosphere, trapping the infrared radiation from the sun and causing an effect called the Greenhouse Effect. This effect contributes to global Climate Change and is a topical issue. Using the K-means clustering algorithm, a model is produced in order to provide a deeper insight into the emissions of the industrial sectors of the UK, France and Italy. The model is intended to be of use to those in governmental authority when decisions on emissions within individual industries are to be made.

Spatio-temporal analysis of Greenhouse Gas data via clustering techniques

Cuzzocrea Alfredo;
2015

Abstract

Data mining allows for hidden patterns to be brought to light in large data sets. This paper aims to apply data mining on real-life data showing Greenhouse Gas Emissions for countries within the European Union. Greenhouse Gasses are gasses which are released into the atmosphere, trapping the infrared radiation from the sun and causing an effect called the Greenhouse Effect. This effect contributes to global Climate Change and is a topical issue. Using the K-means clustering algorithm, a model is produced in order to provide a deeper insight into the emissions of the industrial sectors of the UK, France and Italy. The model is intended to be of use to those in governmental authority when decisions on emissions within individual industries are to be made.
9781479920020
Spatio-temporal data analysis
Clustering
Big data
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11770/312861
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