I. Introduction
Data Clustering has become a challenging task in data mining and machine learning since several real life applications require to organize data into groups based on their similar descriptive characteristics. Examples of these applications are Image Segmentation [1], market segmentation [2], customers segmentation [3], document summarizing[4] and many other applications. The issue of organizing data into groups has been studied during the last three decades. Several clustering methods have been proposed in the literature. Existing methods are based on several approaches, such as Partitioning, Hierarchical, Density based methods and graph based methods [3], [5] to look for groups in data. However, given the exponential growth of data captured from different available sources (social media, sites, mobile devices, on-line videos, etc), most of the existing methods cannot be used for large scale volume of data. The scalability and the ability of the method to perform clustering on big volume of data has become a necessary and important requirement.