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Data mining is the process of discovering useful patterns from large geo-spatial datasets with the help of machine learning methods. . The machine learning methods plays an important role for data analytics modeling and visualization. Geo-spatial data is a significant task in many application domains, such as environmental science, geographic information science, and social networks. However, the existing spatial pattern discovery and prediction techniques failed to predict the event accurately with minimum error and time consumption. . In this paper, a novel Pearson Correlated Regression Tree-based Affine Projective spatial data Classification (PCRT-APSDC) technique is proposed to improve the spatial data classification and minimize error based on the Affine Projective classification technique. The proposed algorithm employs a fuzzy rule procedure that constructs the regression tree. The fuzzy rule is applied for linking the inputs (i.e. spatial data) with the outputs (i.e. classification results). Our goal is to classify the data into two subsets such as fired region and non-fired region. Experimental evaluation is carried out using a forest fire dataset with different factors such as classification accuracy, false-positive rate, and classification time. The results confirm that the proposed technique predicts the fired region with increased spatial data classification accuracy and minimized time as well as false-positive rate than the state-of-the-art methods.
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