Analysis of k-means clustering algorithm in advanced country clustering using rapid miner
Main Article Content
Abstract
In the era of globalization, the understanding of developed countries is no longer limited to the level of per capita income alone. As part of the analysis of developed countries based on aspects of government revenue, income balance, national savings, and domestic output based on sales. This research aims to cluster and to find out how these economic indicators are interrelated and affect the status of a country as a developed country. The K-means algorithm is used to identify patterns of countries with similar economic characteristics. From the research conducted, there are 4 clusters generated based on the characteristics of developed countries.
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
O. Ogrokhina and C. M. Rodriguez, “Inflation targeting and capital flows: A tale of two cycles in developing countries,” J. Int. Money Financ., vol. 146, p. 103121, Aug. 2024, doi: 10.1016/j.jimonfin.2024.103121.
G. L. Kaminsky, C. M. Reinhart, and C. A. Végh, “When It Rains, It Pours: Procyclical Capital Flows and Macroeconomic Policies,” NBER Macroecon. Annu., vol. 19, pp. 11–53, Jan. 2004, doi: 10.1086/ma.19.3585327.
R. J. Caballero and A. Simsek, “A Model of Fickle Capital Flows and Retrenchment,” J. Polit. Econ., vol. 128, no. 6, pp. 2288–2328, Aug. 2019, doi: 10.1086/705719.
E. Cavallo, A. Izquierdo, and J. J. León-Díaz, “Preventing Sudden Stops in Net Capital Flows,” Washington, D.C., Aug. 2020. doi: 10.18235/0002561.
H. M. Ferreira, D. R. Carneiro, M. Â. Guimarães, and F. V. Oliveira, “Supervised and unsupervised techniques in textile quality inspections,” Procedia Comput. Sci., vol. 232, pp. 426–435, 2024, doi: 10.1016/j.procs.2024.01.042.
B. Mohammadi, M. Fathy, and M. Sabokrou, “Image/Video Deep Anomaly Detection: A Survey,” Mar. 2021, [Online]. Available: http://arxiv.org/abs/2103.01739
V. Chandola, A. Banerjee, and V. Kumar, “Anomaly detection,” ACM Comput. Surv., vol. 41, no. 3, pp. 1–58, Jul. 2009, doi: 10.1145/1541880.1541882.
J. Heidari, N. Daneshpour, and A. Zangeneh, “A novel K-means and K-medoids algorithms for clustering non-spherical-shape clusters non-sensitive to outliers,” Pattern Recognit., vol. 155, p. 110639, Nov. 2024, doi: 10.1016/j.patcog.2024.110639.
Yee Leung, Jiang-She Zhang, and Zong-Ben Xu, “Clustering by scale-space filtering,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 12, pp. 1396–1410, 2000, doi: 10.1109/34.895974.
C. C. Aggarwal and C. Zhai, “Text classification,” in Data Classification: Algorithms and Applications, 2014, pp. 287–336. doi: 10.1201/b17320.
W. Wu, W. Wang, X. Jia, and X. Feng, “Transformer Autoencoder for K-means Efficient clustering,” Eng. Appl. Artif. Intell., vol. 133, p. 108612, Jul. 2024, doi: 10.1016/j.engappai.2024.108612.
S. Piqueras et al., “Handling Different Spatial Resolutions in Image Fusion by Multivariate Curve Resolution-Alternating Least Squares for Incomplete Image Multisets,” Anal. Chem., vol. 90, no. 11, pp. 6757–6765, Jun. 2018, doi: 10.1021/acs.analchem.8b00630.
A. Gómez-Sánchez, R. Vitale, C. Ruckebusch, and A. de Juan, “Solving the missing value problem in PCA by Orthogonalized-Alternating Least Squares (O-ALS),” Chemom. Intell. Lab. Syst., vol. 250, p. 105153, Jul. 2024, doi: 10.1016/j.chemolab.2024.105153.
K. P. Sinaga and M.-S. Yang, “Unsupervised K-Means Clustering Algorithm,” IEEE Access, vol. 8, pp. 80716–80727, 2020, doi: 10.1109/ACCESS.2020.2988796.
M. Capo, A. Perez, and J. A. A. Lozano, “An efficient Split-Merge re-start for the K-means algorithm,” IEEE Trans. Knowl. Data Eng., pp. 1–1, 2020, doi: 10.1109/TKDE.2020.3002926.
A. M. Ikotun, A. E. Ezugwu, L. Abualigah, B. Abuhaija, and J. Heming, “K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data,” Inf. Sci. (Ny)., vol. 622, pp. 178–210, Apr. 2023, doi: 10.1016/j.ins.2022.11.139.
A. Bustamam, H. Tasman, N. Yuniarti, Frisca, and I. Mursidah, “Application of k-means clustering algorithm in grouping the DNA sequences of hepatitis B virus (HBV),” 2017, p. 030134. doi: 10.1063/1.4991238.