Analysis (according to Ioannis Th. Mazis theoretical basis) proposes a multi-dimensional, interdisciplinary research pattern, which embraces economic, cultural, political and defensive facts. The amount of data produced combining these attributes is extremely large and complex. One of the solutions to explore and analyze this data is clustering it. In this work, two clustering algorithms were used, namely DBSCAN and the k-means techniques which both of them cluster data according to its characteristics. While DBSCAN groups data based on the minimum size of participating objects per cluster and the minimum required distance between them, k-means clusters the data objects according the pre-desired number of groups. Thus, since the two methods use different roads to group the data objects, they form different clusters but each one has its importance depending on the characteristics of the applied method. As a result, in this work a comparative study is presented. %B Int. J. of Grid and Utility Computing %V 10 %P 1-16 %G eng %N 10