Using Grey Wolf Optimization Algorithm in Big Data Clustering

The huge amount of data created constantly with increasing rate from different sources such as smart phones, social media, imaging technologies and etc. becomes difficult to be analyzed by conventional data analytic tools. For this reason a new field of research called Big Data Analytics is growing faster in the research and industrial communities. Clustering big datasets is one of the important challenges which attracts more and more attentions among researchers. In this paper first a method for non-automatic big data clustering (when the number of clusters is known) and then a two-stage method for big data automatic clustering (able in finding the number of clusters) based on grey wolf optimization algorithm are introduced.  In the first stage the algorithm tries to find the number of clusters using a tree structure and in the second stage the main algorithm searches the solution space to find the position of centroids. The methodology is tested on 13 synthetics and 2 real big mobility datasets. The achieved results show its effectiveness in big data clustering.