Interfacing with the ever-increasing volumes of data in the sectors of technology, medicine, science, engineering, industry, and finance and transforming them into a comprehensible format. One of the primary criteria is a human user. Information-intensive applications will demand effective methods and the ability to learn from fresh data in order to swiftly identify and evaluate intricate patterns and requirements. Classification and clustering of widely accessible data is one way to address this. Within this research, we suggested a two -tier method for clustering big data sets for rain fall data prediction using SOM and SVM with ID3 in order to partially satisfy the market demand. This research examines a novel method for grouping the SOM and SVM with ID3. Specifically, application of agglomerative hierarchical clustering and ID3-participated clustering is examined. When compared to straight clustering of the data, the two-stage process, which first uses SOM to create the illustration and then examines SVM with ID3, performs well and cuts down on computation time.
Rainfall prediction, SOM, SVM, ID3, ANN, Clustering, Forecasting, Entropy, Data mining, Weather data
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