A Data Mining Approach for secure Cloud using Enhanced Random Forest

Data mining is the process of extracting and analyzing the large datasets to find out various hidden relationship patterns and much other useful information. Random forest is an ensemble method which is widely used is application having large datasets because of its interesting features like handling imbalanced data, identifying variable importance and detecting error rate. For building random forest randomness is established in two ways: Firstly by creating samples from original datasets randomly and Secondly at the time of creation of each tree, randomly selecting subsets of attributes at each node for best splitting decisions. But by using randomness in Random forests we are likely to have uninformative attributes which will lead to poor accuracy results and bad performance of the algorithm. In this paper we are providing an improved Feature selection Random Forest that improves the performance of the algorithm in terms of accuracy. In this first we are selecting the good features by applying the consistency on attributes after that we are combining this consistency based feature with the Random forest. Also most of the organizations today are moving towards the cloud computing services, so we are performing the mining operation on the cloud based data. To protect the data from the unauthorized user we are securing the cloud data using AES algorithm through this no unauthorized user can access the data. Keywords— Data mining, cloud computing, Feature selection.