Implementation of K-Means and K-Medians Clustering in Several Countries Based on Global Innovation Index (GII) 2018

Ade Famalika; Pardomuan Robinson Sihombing

doi:10.26877/asset.v3i1.8461

Implementation of K-Means and K-Medians Clustering in Several Countries Based on Global Innovation Index (GII) 2018

Ade Famalika, Pardomuan Robinson Sihombing

Abstract

The Global Innovation Index (GII) is an instrument to assess the ranking of innovation capabilities of all countries. The sub-index of the GII has seven enabler pillars: Institutions, Human Capital and Research, Infrastructure, Market sophistication, Business Sophistication, Knowledge and Technology Outputs, and Creative Outputs. The k-means method and k-medians method are methods for cluster countries based on GII. Cluster 1 in k-means method consists of 48 Countries, Cluster 2 consists of 45 Countries and Cluster 3 consists of 33 Countries and has the average value of seven variables are the highest. Cluster 1 in k-medians method consists of 33 Countries and has the average value of seven variables are the highest., Cluster 2 consists of 53 Countries and Cluster 3 consists of 40 Countries. The result clustering with using k-means method and k-medians method showed that k-medians is better than k-means method because the variance value of k-medians is smaller than k-means.

Keywords

GII; K-Means Cluster; K-Medians Cluster; Variance

Full Text:

PDF

References

Dutta, Soumitra. Lanvin, Bruno, and Wunsch-Vincent, Sacha. 2018. Global Innovation Index 2018. Eleventh edition. WIPO, INSEAD, and Cornell University. ebook

Madhulatha, T.Soni. 2012. An Overview On Clustering Methods. Vol. 2(4) pp: 719-725. arXiv:1205.1117v1

Hair, J. F, Jr. et al. 2006. Multivariate Data Analysis, Sixth Edition. Pearson Prentice Hall: New Jersey

Santoso, S. 2012. Aplikasi SPSS pada Statistik Multivariate, Elex Media Komputindo: Jakarta

Gujarati, D. N., (2004), Basic Econometrics, Fourth Edition. McGraw-Hill: New York.

Chauhan A, Mishra G, and Kumar G. 2011. Survey on Data Mining Techniques in Intrusion Detection vol. 2, no. 7, pp: 2–5.

Riadi I, Istiyanto J E, and Saleh S. 2013. Forensics Framework Based-on Internet Forensics Framework Based-on Clustering.

Eiyanto N S, Mara M N .2013. Characteristics classification by method k-means cluster analysis Bul. Llm., vol.02.,no.2, pp.133-136.

Kaufmann L, & Rousseeuw PJ. 1990. Finding Groups in Data: An Introduction to Cluster Analysis. New York: John Wiley. ebook

Handoyo, Rendy., M, R. Rumani., Nasution, Surya Michrandi. 2014. Perbandingan Metode Clustering Menggunakan Metode Single Linkage dan K-Means Pada Pengelompokkan Dokumen. ISSN. 1412-0100 Vol. 15, No. 2. Pp. 1-10.

Hartigan, J. A. (1975). Clustering Algorithms. New York: John Wiley & Sons.

Rencher, A. C. (2002). Methods of Multivariate Analysis. Canada: John Willey.

Teknomo, K. (2007). K-Means Clustering Tutorial.

Anderson, Gross, Musicant, Ritz, Smith, dan Steinberg, 2006. Adapting K-Medians to Generate Normalized Cluster Centers.

M. Ackermann, J. Blomer, C. Sohler (2010) Clustering for Metric and Non-Metric Distance Measures ACM Transactions on Algorithms 6:4

Bunkers, et al. (1996). Definition of Climate Regions in the Northern Plains Using an Objective Cluster Modification Technique. pp: 130-146. https://doi.org/10.1175/1520-0442(1996)009<0130:DOCRIT>2.0.CO;2

Fathia, Annisa Nur, Rahmawati, Rita, and Tarno. 2016. Analisis Klaster Kecamatan di Kabupaten Semarang Berdasarkan Potensi Desa Menggunakan Metode Ward dan Single Linkage. Vol.5, No.4, pp:801-810. Semarang. https://doi.org/10.14710/j.gauss.v5i4.17109

DOI: https://doi.org/10.26877/asset.v3i1.8461

Refbacks

There are currently no refbacks.

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

Advance Sustainable Science, Engineering and Technology (ASSET)

E-ISSN: 2715-4211
Published by Science and Technology Research Centre

Universitas PGRI Semarang, Indonesia

Website: http://journal.upgris.ac.id/index.php/asset/index
Email: [email protected]