Document Clustering in Military Explicit Knowledge: A Study on Peacekeeping Documents

In Military domain, knowledge can also be categorized into explicit knowledge and tacit knowledge, where the explicit military knowledge can be any form of knowledge that can easily articulated, codified, accessed and stored into various media forms. Further, advanced computer technologies give a convenient platform for digitizing documents, producing web documents and electronic documents, including this explicit military knowledge (e.g. military peacekeeping documents). The main goal here is to discover useful knowledge from military peacekeeping documents. Yet, text mining is a powerful technique that is widely used for discovering useful patterns and knowledge specially in unstructured text documents. This paper describes Text Analytics of Unstructured Data (TAUD) framework for analyzing and discovering significant text patterns exist in the military text documents. The framework consists of three (3) components: (i) data collection (ii) document preprocessing and (iii) text analytics and visualization which are word cloud and document clustering using K-Means algorithm. The findings of this study allow the military commanders and training officers to understand and access the military knowledge which they had learnt and gathered during the training programs before they can be deployed into a peacekeeping mission.

[1]  Mohammed Azmi Al-Betar,et al.  Text feature selection with a robust weight scheme and dynamic dimension reduction to text document clustering , 2017, Expert Syst. Appl..

[2]  Luís Torgo,et al.  Data Mining with R: Learning with Case Studies , 2010 .

[3]  Elizabeth A. Smith.  The role of tacit and explicit knowledge in the workplace , 2001, J. Knowl. Manag..

[4]  Rodrigo Fernandes de Mello,et al.  Persistent homology for time series and spatial data clustering , 2015, Expert Syst. Appl..

[5]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[6]  Roger Lee,et al.  Text Document Clustering: The Application of Cluster Analysis to Textual Document , 2016, 2016 International Conference on Computational Science and Computational Intelligence (CSCI).

[7]  Marko Sarstedt,et al.  A Concise Guide to Market Research: The Process, Data, and Methods Using IBM SPSS Statistics , 2011 .

[8]  Türkay Dereli,et al.  Analysis of patent documents with weighted association rules , 2015 .

[9]  Ido Dagan,et al.  Knowledge Discovery in Textual Databases (KDT) , 1995, KDD.

[10]  Mehmet Gönen,et al.  Localized Data Fusion for Kernel k-Means Clustering with Application to Cancer Biology , 2014, NIPS.

[11]  Zuraini Zainol,et al.  Establishing of knowledge based framework for situational awareness using Nonaka's and Endsley's models , 2016, 2016 International Conference on Information and Communication Technology (ICICTM).

[12]  Jianbo Shi,et al.  Machine Learning of Hierarchical Clustering to Segment 2D and 3D Images , 2013, PloS one.

[13]  Omar Zakaria,et al.  Text analytics of unstructured textual data: A study on military peacekeeping document using R text mining package , 2017 .

[14]  Aytug Onan,et al.  An improved ant algorithm with LDA-based representation for text document clustering , 2017, J. Inf. Sci..

[15]  Peter J. Rousseeuw,et al.  Finding Groups in Data: An Introduction to Cluster Analysis , 1990 .

[16]  Shalu Gupta,et al.  Data Mining and Data Warehousing , 2012 .

[17]  Zuraini Zainol,et al.  Keyword based Clustering Technique for Collections of Hadith Chapters , 2016 .

[18]  Mohd Afizi Mohd Shukran,et al.  Information Technology Knowledge Management in Malaysian Armed Forces , 2012 .

[19]  Amish Desai,et al.  A Review on Knowledge Discovery using Text Classification Techniques in Text Mining , 2015 .

[20]  Nohuddin Puteri N.E,et al.  Knowledge management in military: A review for Malaysian Armed Forces’ communities of practices , 2010 .