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2013 (Vol. 5, Issue: 14)
Article Information:

Association Rule Mining and Classifier Approach for 48-Hour Rainfall Prediction Over Cuddalore Station of East Coast of India

S. Meganathan and T.R. Sivaramakrishnan
Corresponding Author:  S. Meganathan 

Key words:  Apriori algorithm, association mining, classification, K* algorithm, rainfall prediction, ,
Vol. 5 , (14): 3692-3696
Submitted Accepted Published
July 05, 2012 July 31, 2012 April 20, 2013

The methodology of data mining techniques has been presented for the rain forecasting models for the Cuddalore (1143′ N/7949′ E) station of Tamilnadu in East Coast of India. Data mining approaches like classification and association mining was applied to generate results for rain prediction before 48 hour of the actual occurrence of the rain. The objective of this study is to demonstrate what relationship models are there between various atmospheric variables and to interconnect these variables according to the pattern obtained out of data mining technique. Using this approach rainfall estimates can be obtained to support the decisions to launch cloud-seeding operations. There are 3 main parts in this study. First, the obtained raw data was filtered using discretization approach based on the best fit ranges. Then, association mining has been performed on it using Predictive Apriori algorithm. Thirdly, the data has been validated using K* classifier approach. Results show that the overall classification accuracy of the data mining technique is satisfactory
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  Cite this Reference:
S. Meganathan and T.R. Sivaramakrishnan, 2013. Association Rule Mining and Classifier Approach for 48-Hour Rainfall Prediction Over Cuddalore Station of East Coast of India.  Research Journal of Applied Sciences, Engineering and Technology, 5(14): 3692-3696.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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