Perbandingan Akurasi Euclidean Distance, Minkowski Distance, dan Manhattan Distance pada Algoritma K-Means Clustering berbasis Chi-Square

M Nishom

Abstract


In data mining, there are several algorithms that are often used in grouping data, including K-Means. However, this method still has several disadvantages, including the problem of the level of accuracy of the methods used to measure the similarities between the objects being compared. To overcome this problem, in this study a comparison was made between three methods (euclidean distance, manhattan distance, and minkowski distance) to determine the status of disparity in Teacher's needs in Tegal City. The results showed that of the three methods compared had a good level of accuracy, which is 84.47% (for euclidean distance), 83.85% (for manhattan distance), and 83.85% (for minkowski distance). In addition, this study also informs that there are still 6 (six) schools with conditions that are very poorly available for teachers (in the category of HIGH disparity labels) and need to get more attention, which is SMP Atmaja Wacana, SMKN 3 Tegal, SMAS Muhammadiyah, SMAS Pancasakti Tegal, SMKS Muhammadiyah 1 Kota Tegal, and SMP IC Bias Assalam.

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References


P.-N. Tan, M. Steinbach, A. Karpatne, and V. Kumar, Introduction to Data Mining (2nd Edition), 2nd ed. New York: Pearson, 2018.

M. Nishom, “Implementasi Metode K-Means berbasis Chi-Square pada Sistem Pendukung Keputusan untuk Identifikasi Disparitas Kebutuhan Guru,” J. Sist. Inf. Bisnis, vol. 8, no. 2, pp. 1–8, 2018.

S. Saraswathi and M. I. Sheela, “A Comparative Study of Various Clustering Algorithms in Data Mining,” vol. 3, no. 11, pp. 422–428, 2014.

R. Awasthi, A. K. Tiwari, and S. Pathak, “Empirical Evaluation On K Means Clustering With Effect Of Distance Functions For Bank Dataset,” Int. J. Innov. Technol. Res., vol. 1, no. 3, pp. 233–235, 2013.

A. Singh, A. Rana, and A. Yadav, “K-means with Three different Distance Metrics,” Int. J. Comput. Appl., vol. 67, no. 10, pp. 13–17, 2013.

K. Kouser, “A comparative study of K Means Algorithm by Different Distance Measures,” Int. J. Innov. Res. Comput., vol. 1, no. 9, pp. 2443–2447, 2013.

D. Sinwar and R. Kaushik, “Study of Euclidean and Manhattan Distance Metrics using Simple K-Means Clustering,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 2, no. 5, pp. 270–274, 2014.

D. J. Bora and A. K. Gupta, “Effect of Different Distance Measures on the Performance of K-Means Algorithm: An Experimental Study in Matlab,” Eff. Differ. Distance Meas. Perform. K-Means Algorithm An Exp. Study Matlab, vol. 5, no. 2, pp. 2501–2506, 2014.

H. Prasetyo and A. Purwariati, “Comparison of Distance Measures for Clustering Data with Mix Attribute Types,” in International Conference on Information Technology Systems and Innovation, 2014.

A. Singh, J. Agarwal, and A. Rana, “Performance Measure of Similis and FPGrowth Algo rithm,” Int. J. Comput. Appl., vol. 62, no. 6, pp. 25–31, 2013.

H. Anton, Elementary Linear Algebra, 7th ed. New Jersey: Wiley, 1993.

R. Stine, Statistics for Business Decision Making and Analysis with Chi-Square Tests. New York: Pearson, 2011.




DOI: https://doi.org/10.30591/jpit.v4i1.1253

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Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.