Abstract
In this article we investigate an impact of inclusion of different data types into a clustering process. As a case-study we use reports from the EWID database which is a system used by Polish State Fire Service for documenting incidents. Each incident reported in that database is characterized by a set of quantitative attributes and by natural language descriptions of the cause, scene and the course of actions undergone by firefighters. We show that the utilization of both of those data types for a clustering purpose can be beneficial in terms of semantic homogeneity of the resulting groups. We argue that such clusters might serve as a useful tool in the firefighters’ training process.
This work was partially supported by the Polish National Science Centre grants 2011/01/B/ST6/03867 and 2012/05/B/ST6/03215, and by the Polish National Centre for Research and Development (NCBiR) - grant O ROB/0010/03/001 under Defence and Security Programmes and Projects: “Modern engineering tools for decision support for commanders of the State Fire Service of Poland during Fire&Rescue operations in buildings”.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Rousseeuw, P.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics 20, 53–65 (1987)
Caliński, T., Harabasz, J.: A dendrite method for cluster analysis. Communications in Statistics-Theory and Methods 3(1), 1–27 (1974)
Holmes, M., Wang, Y., Ziedins, I.: The application of data mining tools and statistical techniques to identify patterns and changes in fire events. Technical Report 95, New Zealand Fire Service, Auckland, NZ (May 2009)
Xiangxin, L.: Rational judging method of fire station layout based on data mining. In: 2nd IEEE International Conference on Emergency Management and Management Sciences (ICEMMS), pp. 455–458. IEEE (2011)
Krasuski, A., Kreński, K., Łazowy, S.: A method for estimating the efficiency of commanding in the State Fire Service of Poland. Fire Technology 48(4), 795–805 (2012)
Kreński, K., Krasuski, A., Łazowy, S.: Data mining and shallow text analysis for the data of State Fire Service. In: Proceedings of Concurrency, Specification and Programming - XXth International Workshop, CS&P 2011, Białystok University of Technology, pp. 313–321 (2011)
Krasuski, A., Ślęzak, D., Kreński, K., Łazowy, S.: Granular knowledge discovery framework. In: Pechenizkiy, M., Wojciechowski, M. (eds.) New Trends in Databases & Inform. AISC, vol. 185, pp. 109–118. Springer, Heidelberg (2012)
Elzinga, P., Poelmans, J., Viaene, S., Dedene, G., Morsing, S.: Terrorist threat assessment with formal concept analysis. In: Proceedings of the 2010 IEEE International Conference on Intelligence and Security Informatics (ISI), pp. 77–82. IEEE (2010)
Poelmans, J., Elzinga, P., Viaene, S., Hulle, M.M.V., Dedene, G.: Gaining insight in domestic violence with emergent self organizing maps. Expert Syst. Appl. 36, 11864–11874 (2009)
Poelmans, J., Elzinga, P., Dedene, G., Viaene, S., Kuznetsov, S.: A concept discovery approach for fighting human trafficking and forced prostitution. In: Andrews, S., Polovina, S., Hill, R., Akhgar, B. (eds.) ICCS-ConceptStruct 2011. LNCS, vol. 6828, pp. 201–214. Springer, Heidelberg (2011)
Szczuka, M.S., Janusz, A.: Semantic clustering of scientific articles using explicit semantic analysis. T. Rough Sets 16, 83–102 (2013)
Graeger, A., Cimolino, U., de Vries, H., Sümersen, J.: Einsatz-und Abschnittsleitung: Das Einsatz-Führungs-System (EFS). Ecomed Sicherheit (2009)
Krasuski, A., Wasilewski, P.: The Detection of Outlying Fire Service’s Reports. The FCA Driven Analytics. In: Cellier, P., Distel, F. (eds.) Contributions to the 11th International Conference on Formal Concept Analysis, TU Dresden, pp. 35–50 (2013)
R Development Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2008)
Kaufman, L., Rousseeuw, P.: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley Interscience, New York (1990)
Janusz, A., Ślęzak, D., Nguyen, H.S.: Unsupervised similarity learning from textual data. Fundamenta Informaticae 119(3), 319–336 (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Janusz, A., Krasuski, A., Szczuka, M. (2013). Improving Semantic Clustering of EWID Reports by Using Heterogeneous Data Types. In: Ciucci, D., Inuiguchi, M., Yao, Y., Ślęzak, D., Wang, G. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2013. Lecture Notes in Computer Science(), vol 8170. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41218-9_33
Download citation
DOI: https://doi.org/10.1007/978-3-642-41218-9_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41217-2
Online ISBN: 978-3-642-41218-9
eBook Packages: Computer ScienceComputer Science (R0)