A novel linear assorted classification method based association rule mining with spatial data

Abstract

Spatial data classification and extraction is a significant problem to be resolved in data mining. The classification performance of existing techniques was not effectual to accurately mine the interesting spatial data. Furthermore, the amount of time taken for classifying the spatial data location was very higher. In order to resolve the above limitations, an Exponentiated Pareto based linear assorted classification method is introduced to reduce the incorrect classification of spatial data. This paper begins with a discussion of traditional methods of spatial data mining. The algorithm makes an association rule with spatial data objects. The technique conducts the experimental works using metrics such as classification accuracy, time complexity, space complexity and false positive rate with respect to different number of data. The proposed approach takes the forest fire dataset and El Nino dataset as input and predicts the burned area of forest fires and weather and climate conditions. The experimental results show that the proposed technique is able to increase the classification accuracy and reduce the time complexity as well as space complexity and false positive rate of spatial data mining as compared to state-of-the-art works.