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

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Advances in Visual Informatics (IVIC 2017)

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Abstract

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.

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References

  1. United Nation peacekeeping (2017) http://www.un.org/en/peacekeeping/. Accessed 20 June 2017

  2. Yusof, W.S.E.Y.W., Zakaria, O., Zainol, Z.: Establishing of knowledge based framework for situational awareness using Nonaka’s and Endsley’s models. In: International Conference on Information and Communication Technology, pp. 47–50. IEEE Xplore (2016). 10.1109/ICICTM.2016.7890775

  3. Smith, E.A.: The role of tacit and explicit knowledge in the workplace. J. Knowl. Manage. 5(4), 311–321. MCG University Press (2010). ISSN 1367–3270

    Google Scholar 

  4. Nohuddin, P.N., et al.: Knowledge management in military: a review for Malaysian armed forces’ communities of practices. J. Converg. Inf. Technol. 7(6), 178–184. Advanced Institute of Convergence Information Technology Research Center, Malaysia (2010). doi:10.4156/jcit.vol7.issue6.22

  5. Feldman, R., Dagan, I.: Knowledge discovery in textual databases (KDT). In: KDD. vol. 95, pp. 112–117 (1995)

    Google Scholar 

  6. Shrihari, R.C., Desai, A.: A review on knowledge discovery using text classification techniques in text mining. Int. J. Comput. Appl. 111(6), 12–15 (2015)

    Google Scholar 

  7. Massey, G.: Extracting relevance from unstructured medical data. http://www.psqh.com/analysis/in-context-extracting-relevance-from-unstructured-medical-data/

  8. Mooi, E., Sarstedt, M.: Understanding cluster-analysis. In: A Concise Guide to Market Research. The Process, Data, and Methods Using IBM SPSS Statistics, pp. 259–283. Springer, Heidelberg/Dordrecht (2011)

    Google Scholar 

  9. Mourya, S., Gupta, S.: Data Mining and Data Warehousing. Alpha Science International, Ltd., Oxford (2012)

    Google Scholar 

  10. Altuntas, S., Dereli, T., Kusiak, A.: Analysis of patent documents with weighted association rules. Technol. Forecast. Soc. Change 92, 249–262 (2015). Elsevier

    Article  Google Scholar 

  11. Han, J., Pei, J., Kamber, M.: Data Mining: Concepts and Techniques. Elsevier, Amsterdam (2011)

    MATH  Google Scholar 

  12. Gönen, M., Margolin, A.A.: Localized data fusion for kernel k-means clustering with application to cancer biology. In: Proceedings of Advances in Neural Information Processing Systems, pp. 1305–1313. MIT Press, Cambridge (2014)

    Google Scholar 

  13. Nunez-Iglesias, J., et al.: Machine learning of hierarchical clustering to segment 2d and 3d images. PLoS One 8(8), e71715 (2013). doi:10.1371/journal.pone.0071715

    Article  Google Scholar 

  14. Tan, P.N., Steinbach, M., Kumar, V.: Data Mining Cluster Analysis: Basic Concepts and Algorithms. Pearson Addison-Wesley, Boston (2006)

    Google Scholar 

  15. Pereira, C.M., de Mello, R.F.: Persistent homology for time series and spatial data clustering. Expert Syst. Appl. 42(15), 6026–6038 (2015). Elsevier

    Article  Google Scholar 

  16. Du, H.: Data Mining Techniques and Applications: An Introduction. Cengage Learning, Boston (2010)

    Google Scholar 

  17. Nohuddin, P.N., et al.: Keyword based clustering technique for collections of hadith chapters. Int. J. Islamic Appl. Comput. Sci. Technol. (IJASAT) 4(3), 11–18 (2015)

    Google Scholar 

  18. Reddy, V.S., Kinnicutt, P., Lee, R.: Text document clustering: the application of cluster analysis to textual document. In: International Conference on Computational Science and Computational Intelligence. IEEE (2016)

    Google Scholar 

  19. Abualigah, L.M., et al.: Text feature selection with a robust weight scheme and dynamic dimension reduction to text document clustering. Expert Syst. Appl. 84, 24–36 (2017). ACM

    Article  Google Scholar 

  20. Onan, A., Bulut, H., Korukoglu, S.: An improved ant algorithm with LDA-based representation for text document clustering. J. Inf. Sci. 43(2), 275–292 (2017)

    Article  Google Scholar 

  21. Introduction to clustering. https://www.datascience.com/blog/introduction-to-k-means-clustering-algorithm-learn-data-science-tutorials

  22. Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, Hoboken (2009)

    MATH  Google Scholar 

  23. Torgo, L.: Data Mining with R: Learning with Case Studies. Chapman and Hall/CRC, Boca Raton (2011)

    Google Scholar 

  24. Zainol, Z., et al.: Text analytics of unstructured textual data: a study on military peacekeeping document using R text mining package. In: International Conference on Computing and Informatics, pp. 1–7. School of Computing, UUM (2017)

    Google Scholar 

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Acknowledgements

The authors would like to thank Universiti Pertahanan Nasional Malaysia (UPNM) and Kementerian Pendidikan Malaysia (KPM) under NRGS/2013/UPNM/PK/P3 for sponsoring this publication.

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Correspondence to Zuraini Zainol .

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Zainol, Z., Marzukhi, S., Nohuddin, P.N.E., Noormaanshah, W.M.U., Zakaria, O. (2017). Document Clustering in Military Explicit Knowledge: A Study on Peacekeeping Documents. In: Badioze Zaman, H., et al. Advances in Visual Informatics. IVIC 2017. Lecture Notes in Computer Science(), vol 10645. Springer, Cham. https://doi.org/10.1007/978-3-319-70010-6_17

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  • DOI: https://doi.org/10.1007/978-3-319-70010-6_17

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-70010-6

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