Big data refers to the generation of a huge volume of data continuously. Hence, analytics on such as large volume of data is becoming more complex regarding more time consumption and memory usage. With the aim of enhancing prediction accuracy by lesser time consumption, Decision Stump Feature Selection based Mean Shift Brown Boost Map Reduce Data Clustering (DSFS-MSBBMPDC) Technique is introduced for analyzing the spatial data to predict the future results. DSFS-MSBBMPDC technique consists of various procedures such as feature selection and clustering process to predictfuture results. First, the Otsuka-Ochiai decision stump Feature Selection was performed for choosing significant features. By one internal node, decision tree is linked to terminal node. After feature selection, the mean Shift steepest descent Brown Boost Map Reduce Data clustering process was performed to group input data to perform spatial data analysis. Brown Boost cluster combines the weak learner to form strong cluster. The prediction accuracy was increased as well as prediction error was reduced using the steepest descent function. The simulation is achieved by geographical dataset with various parameters by amount of features and amount of data. DSFS-MSBBMPDC improves performance compared with state-of-the-art works.

Predictive Analytics, big data, Otsuka-Ochiai decision stump, Brown Boost cluster, Mean Shift Clustering, steepest descent function