THE POSSIBILITIES OF CLUSTERING LEARNING METHODS IN STUDENT EDUCATION

Pēteris Grabusts

Abstract


Many educational courses operate with models that were previously available only in mathematics or other learning disciplines. As a possible solution, there could be the use of package IBM SPSS Statistics and Modeler in realization of different algorithms for IT studies. Series of research were carried out in order to demonstrate the suitability of the IBM SPSS for the purpose of visualization of various simulation models of some data mining disciplines – particularly cluster analysis. Students are very interested in modern data mining methods, such as artificial neural networks, fuzzy logic and clustering. Clustering methods are often undeservedly forgotten, although the implementation of their algorithms is relatively simple and can be implemented even for students. In the research part of the study the modelling capabilities in data mining studies, clustering algorithms and real examples are demonstrated.


Keywords


clustering; data analysis; modelling; simulation; SPSS; SPSS Modeler; learning

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References


Agrawal, R., Faloutsos, C., & Swami, A. (1993). Efficient similarity search in sequence databases. Proc. 4th Int. Conf. On Foundations of Data Organizations and Algorithms, Chicago, 69-84.

Aggarwal, C. & Reddy, C. (2013). Data clustering: Algorithms and applications. Chapman and Hall/CRC.

Everitt, B. (1993). Cluster analysis. Edward Arnold, London.

Gan, G. (2007). Data clustering: Theory, algorithms and applications. ASA-SIAM series on Statistics and Applied Probability, SIAM, Philadelphia, ASA, Alexandria, VA.

Han, J. (2001). Geographic Data Mining and Knowledge Discovery. Taylor and Francis, 372 pages.

IBM SPSS Statistics (2018). Retrieved from https://www.ibm.com/lv-en/marketplace/spss-statistics

IBM SPSS Modeler (2018). Retrieved from https://www.ibm.com/products/spss-modeler

Kaufman, L. & Rousseau, P. (2005). Finding groups in data. An introduction to cluster analysis. John Wiley & Sons.

Li, M. (2004). The similarity metric. IEEE Transactions on Information Theory, vol.50, No. 12, 3250-3264.

Vitanyi, P. (2005). Universal similarity. ITW2005, Rotorua, New Zealand.

Xu, R. & Wunch, D. (2009). Clustering. John Wiley & Sons.

Wierzchon, S. & Klopotek, M. (2018). Modern Algorithms of Cluster Analysis. Springer.




DOI: https://doi.org/10.17770/sie2019vol5.3723

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