Data leak identification in social networks using hybrid tranquility K means clustering

Abstract

Loss of sensitive data can be stopped employing Data Leak Prevention (DLP). Most of such tools can be quite effective while protecting private information known already. At the same time, plenty of private information has not been recognized until it has been disclosed to various unknown users or other competition enterprises. Clustering refers to a data mining technique that can classify a certain set of instances into different clustersusing a measure of similarity. One of the most common algorithms based on partitioning is the K-Means. However, it has many drawbacks like it can generate local optimal solutions that are based on initial centroids that are chosen randomly. The Tabu Search (TS) Tranquility Search and the Stochastic Diffusion Search (SDS) have been proposed in this work. In one of the most recent algorithms called Tranquility Search, optimal global solutions are obtained by exploring through the entire solution space. Some studies show hybrid algorithms that are a combination of two different ideas producing better solutions. In this work, a new approach is presented which is a combination of two different ideas producing better solutions. The Improved Tranquility Search technique and the K-means algorithm are combined. For this, a hybrid Tranquility-TABU-SDS algorithm is applied in the social network for the DLP. The results of the experiment have proved that the method proposed performs better in comparison to other methods.