A Data Analysis of the Impact of Natural Disaster in West Java Indonesia Using K-means Clustering Algorithm of Data Mining Technique

Prihandoko Prihandoko

Abstract


Natural disasters are often happened in Indonesia, where most of them cause serious damages to the country. Most natural disasters are triggered by the weather conditions which are not anticipated before it takes place. The impact of the disaster is quite terrible. The victims of the disasters, for some occasions, are quite high in terms of the number of deaths, missing people, injuries, sufferings and the number of refugees. This research analyses the data of the victims for the last five years, obtained from official institutions that related to the weather conditions at the time the disaster happened. The analysis carried out using data mining technique, i.e. k-means algorithm of clustering. The result of the research shows that the weather condition is only the trigger of the disaster, not the main factor causing the high number of victims. 


Keywords


data mining, k-means algorithm, clustering

References


Freeman, P. K., Keen, M., & Mani, M. (2003). Being prepared: Natural disasters are becoming more frequent, more destructive, and deadlier, and poor countries are being hit the hardest. Finance and Development, 40(3), 42-45.

Henderson, L. (2004). Emergency and disaster: Pervasive risk and public bureaucracy in developing nations. Public Organization Review: A Global Journal, 4, 103-119.

The World Bank and GFDRR, Indonesia Advancing a National Disaster Risk Financing Strategy – Options for Consideration. http://reliefweb.int/sites/ reliefweb.int/ les/resources/INDONESIA_ DRFI_M9.pdf

Han, J., & Kamber, M. (2006). Data mining: Concepts and techniques (2nd ed.). San Francisco: Morgan Kaufmann.

Hand, D. J., Manilla, H., & Smyth, P. (2001). Principles of Data Mining. Cambridge, MA: MIT Press.

Witten, I. H. (2004). Text mining. In M. P. Singh (Ed.), Practical handbook of Internet computing (pp. 14-1–14-22). Boca Raton, FL: CRC Press.

Macqueen, J. (1967). Some methods for classification and analysis of multivariate obser- vations. In Proceedings of the 5th Berkeley symposium on mathematical statistics and probability, volume 1, pages 281–297. Berkeley, CA: University of California Press.

Belal Al-Zoubi, Al-Zoubi, Amjad Hudaib, Ammar Huneiti and Bassam Hammo. “New Efficient Strategy to Accelerate k-Means Clustering Algorithm”. American Journal of Applied Sciences 5(9) 1247-1250, Science Publications. 2008.

Ball, G. and D. Hall, “A clustering technique for summarizing multivariate data”, (ISODATA), Behav Sci., vol. 12, pp. 153-155, 1967.

Fissher, D.: Knowledge Acquisition via Incremental Conceptual Clustering. Machine Learning, Vol.2, No. 2 (1987) 139-172.

Bahmani, B., Firouzi, T. Niknam, and M. Nayeripour. “A New Evolutionary Algorithm for Cluster Analysis”. Proceedings of world Academy of Science, Engineering and Technology Vol. 36, Dec.2008.

Jain, A. and Dubes, R. (1988). Algorithms for Clustering Data. Englewood Cliffs, NJ: Prentice–Hall.

Hartigan, J. (1975). Clustering Algorithms. Toronto: John Wiley & Sons.

Hartigan, J. and Wong, M. (1979). Algorithm AS136: A k-means clustering algorithm. Applied Statistics, 28(1):100–108.




DOI: http://dx.doi.org/10.21107/kursor.v8i4.1589

Refbacks

  • There are currently no refbacks.


    Informatika - UTM