Defining the correct number of clusters is one of the most fundamental tasks in graph clustering. When it comes to large graphs, this task becomes more challenging because of the lack of prior information. This paper presents an approach to solve this problem based on the Bat Algorithm, one of the most promising swarm intelligence based algorithms. We chose to call our solution, "Bat-Cluster (BC)." This approach allows an automation of graph clustering based on a balance between global and local search processes. The simulation of four benchmark graphs of different sizes shows that our proposed algorithm is efficient and can provide higher precision and exceed some best-known values.

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In the information era, people's lives are deeply impacted by IT via exposure to social media, emails, RSS feed, chats, web pages, etc. Such data is considered very valuable nowadays since it may help companies to better their strategies. For example, companies can analyse their customers' trends or their competitors marketing interventions and adjust their strategies accordingly. Several decisional tools have been developed but most of them rely on relational databases. This makes it difficult for decision makers to take advantage of unstructured data which today represents more than 85% of the available data. Thus, there is a rising need for a suitable management process of unstructured data through collecting, managing, transferring and transforming it into a meaningful informed data. This paper will introduce a new tool for Big Unstructured Data for the Competitive Intelligence named Xplor EveryWhere (XEW). It will also describe the enhancement brought to its newest feature XEWGraph. This tool, or as described later on the paper, this "Service", offers the decision makers the possibility to have a better user experience regarding large graph visualization on their web browsers as well as their mobile devices.