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Title:
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SELF-ORGANIZING DATA MINING FOR WEATHER FORECASTING |
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Author(s):
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Godfrey C. Onwubolu , Petr Buryan , Sitaram Garimella , Visagaperuman Ramachandran , Viti Buadromo , Ajith Abraham |
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ISBN:
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978-972-8924-40-9 |
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Editors:
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Jörg Roth, Jairo Gutiérrez and Ajith P. Abraham (series editors: Piet Kommers, Pedro Isaías and Nian-Shing Chen) |
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Year:
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2007 |
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Edition:
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Single |
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Type:
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Full Paper |
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First Page:
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81 |
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Last Page:
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88 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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The rate at which organizations are acquiring data is exploding and managing such data so as to infer useful knowledge
that can be put to use is increasingly becoming important. Data Mining (DM) is one such technology that is employed in
inferring useful knowledge that can be put to use from a vast amount of data. This paper presents the data mining activity
that was employed in weather data prediction or forecasting. The self-organizing data mining approach employed is the
enhanced Group Method of Data Handling (e-GMDH). The weather data used for the DM research include daily
temperature, daily pressure and monthly rainfall. Experimental results indicate that the proposed approach is useful for
data mining technique for forecasting weather data. |
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