Visualizing Military Explicit Knowledge using Document Clustering Techniques

Speed of decision making, increased operations tempo and enhanced situational awareness are some of the essential characteristics required by United Nations Peacekeeping forces. These can be efficiently achieved by leveraging with enhanced information processing technologies. The process of discovering essential information from the explicit knowledge and its digitization producing web and electronic documents will produce a more effective and efficient decision making environment, enhancing the value of the decision making process. The current work deals with discovering and visualizing useful patterns and knowledge especially in unstructured text, within the domain of United Nations Peacekeeping operations. Text analytics is a powerful technique that helps in achieving the aforementioned goals. Text Analytics of Unstructured Data (TAUD) framework is utilized for analyzing, discovering and visualizing essential patterns and knowledge within the military text documents. The framework focuses on data collection, pre-processing, text analytics and visualization using the hierarchical cluster analysis and K-Means algorithm. The results upon verification indicate that the technique has successfully extracted important knowledge areas or information required by the troops before being deployed in the ground operations. It will substantially help the military commanders and training officers to have an easy and efficient access to all the essential military knowledge before and during deployment on an assignment. Hence, it may lead to an efficient and enhanced situational awareness and decision making during a peacekeeping mission.