An Efficient Machine Learning Regression Model for Rainfall Prediction

Interfacing through the continuously rising amounts of data in technical, medical, scientific, engineering, industrial and monetary fields and their renovation to logical form for the human user is one of the main requirements. To quickly discover and analyze complex patterns and requirements, we need the efficient techniques and need to learn from new data will be necessary for information-intensive applications. One of the solutions for this is that classification and clustering of largely available data. To partially fulfill the industry requirement, in this paper we proposed a two-level approach for clustering large data set for rain fall data prediction with Self Organized Maps (SOM) and Support Vector Machine (SVM) with ID3. In this paper, a novel approach to clustering of the SOM and SVM with ID3 are considered. In particular, the use of hierarchical agglomerative clustering and partitioned clustering with ID3 are investigated. The two-stage procedure first using SOM to produce the prototypes and later it considers the SVM with ID3, that are then clustered in the second stage is found to perform well when compared with direct clustering of the data and to reduce the computation time.