A Novel Data Classification using Fuzzy C-Means Clustering with Privacy Preserving Data Mining

In data mining, clustering is a method of grouping data into different groups, so that the data in each group share similar trends and patterns. The fuzzy clustering is a promising approach that works by assigning membership to each data point corresponding to each cluster center on the basis of distance between the cluster center and the data point. The main focus of this paper is to hide certain confidential data so that they cannot be discovered through data mining techniques. In this work, the Fuzzy C-means algorithm is used for clustering the medical data set based on various attributes by preserving the privacy of the data by AES encryption and decryption method. Here, SPARCS Medical data set is used for processing and the system is developed using MATLAB. It is found that the proposed system reduces the computation time, increases the accuracy and F-measure is used to validate the quality of the cluster obtained.