Evaluation of Distance Measurement Using Complete Linkage Method
DOI:
https://doi.org/10.32877/bt.v6i2.1045
Keywords:
Complete Linkage Method, Canberra Metric, Czekanowski Coefficient, Euclidean Distance
Abstract
Cluster analysis is the process of grouping a number of objects based on information obtained from data that explains the relationship between objects with the principle of maximizing similarities between members of one cluster and minimizing similarities between clusters. Cluster analysis is useful for identifying objects (recognition), supporting decision-making systems, and data mining. Cluster analysis consists of hierarchical (Average Linkage, Single Linkage, Complete Linkage, Ward's, and Centroid) and non-hierarchical (K-Means) methods. Each method generally has advantages and disadvantages. Apart from that, there are several distance measures that are commonly used in the grouping process, such as Euclidean, Canberra Metric, Czekanowski Coefficient, and others. In general, researchers will choose one or several cluster analysis methods as a comparison and a certain distance measure to be applied to the data in order to group objects based on certain criteria. In this research, a study and evaluation of Euclidean distance measures, Canberra Metric, and Czekanowski Coefficient were carried out using the Complete Linkage method based on simulated data. The conclusion obtained from evaluating measures of object similarity, namely Euclidean distance, Canberra Metric, and Czekanowski Coefficient by applying the Complete Linkage method, concluded that Euclidean distance is better used as a measure of object similarity in grouping cases compared to Canberra Metric and Czekanowski Coefficient.
Downloads
References
“Practical Multivariate Analysis - Abdelmonem Afifi, Susanne May, Robin Donatello, Virginia A. Clark - Google Buku.” https://books.google.co.id/books?hl=id&lr=&id=D5K3DwAAQBAJ&oi=fnd&pg=PP1&dq=multivariate+analysis&ots=vU-LkLdOhc&sig=oIjlZAGGIVWQU9Pc6b6dx5kGm44&redir_esc=y#v=onepage&q=multivariate analysis&f=false (accessed Dec. 05, 2023).
“Approaching Multivariate Analysis, 2nd Edition: A Practical Introduction - Pat Dugard, John Todman, Harry Staines - Google Buku.” https://books.google.co.id/books?hl=id&lr=&id=5hh1EAAAQBAJ&oi=fnd&pg=PP1&dq=multivariate+analysis&ots=IsLcpTLwkJ&sig=tqhp_THm7ZOOp6O4NanF53HlyfE&redir_esc=y#v=onepage&q=multivariate analysis&f=false (accessed Dec. 05, 2023).
“Model-Based Clustering and Classification for Data Science: With ... - Charles Bouveyron, Gilles Celeux, T. Brendan Murphy, Adrian E. Raftery - Google Buku.” https://books.google.co.id/books?hl=id&lr=&id=ldGoDwAAQBAJ&oi=fnd&pg=PR15&dq=multivariate+analysis+of+cluster+analysis+complete+linkage&ots=6Mh5-zLn4n&sig=jrMIWAMC5lT1jXA8DnUzdDtpFJU&redir_esc=y#v=onepage&q=multivariate analysis of cluster analysis complete linkage&f=false (accessed Dec. 05, 2023).
A. Kassambara, “Multivariate Analysis I Practical Guide To Cluster Analysis in R Unsupervised Machine Learning”, Accessed: Nov. 20, 2023. [Online]. Available: http://www.sthda.com
B. S. Everitt, S. Landau, and M. Leese, “Cluster Analysis Arnold,” A Memb. Hodder Headl. Group, London, p. 237, 2001, Accessed: Nov. 20, 2023. [Online]. Available: https://books.google.com/books/about/Cluster_Analysis.html?hl=id&id=htZzDGlCnQYC
R. Scitovski, K. Sabo, F. Martínez-Álvarez, and Š. Ungar, “Cluster Analysis and Applications,” Clust. Anal. Appl., pp. 1–271, Jan. 2021, doi: 10.1007/978-3-030-74552-3/COVER.
A. Jaeger and D. Banks, “Cluster analysis: A modern statistical review,” Wiley Interdiscip. Rev. Comput. Stat., vol. 15, no. 3, p. e1597, May 2023, doi: 10.1002/WICS.1597.
A. Fikri, B. F. Hutabarat, and U. Khaira, “Komparasi K-Means Clustering Dan Complete Linkage Dalam Pengelompokan Penyaluran Pinjaman Financial Technology,” J. Ilm. Media Sisfo, vol. 17, no. 2, pp. 228–239, Oct. 2023, doi: 10.33998/MEDIASISFO.2023.17.2.1373.
Y. Reinaldi, N. Ulinnuha, T. Hartono, and M. Hafiyusholeh, “Comparison of Single Linkage, Complete Linkage, and Average Linkage Methods on Community Welfare Analysis in Cities and Regencies in East Java,” J. Mat. Stat. dan Komputasi, vol. 18, no. 1, pp. 130–140, Sep. 2021, doi: 10.20956/J.V18I1.14228.
S. Thaib and R. D. Bekti, “PENERAPAN ANALISIS KLUSTER HIERARKI MENGGUNAKAN METODE AVERAGE, SINGLE, DAN COMPLETE LINKAGE PADA DATA PASIEN COVID-19 DI INDONESIA (Studi Kasus : Data IHSG Tahun 2016 – 2021),” J. Stat. Ind. dan Komputasi, vol. 7, no. 2, pp. 23–30, 2022, Accessed: Nov. 20, 2023. [Online]. Available: https://ejournal.akprind.ac.id/index.php/STATISTIKA/article/view/4389
D. Amelia, G. Kholijah, F. Sains dan Teknologi, and U. Jambi, “Analisis Cluster Pengelompokan Provinsi di Indonesia Berdasarkan Sub Sektor Nilai Tukar Petani,” J. Demogr. Ethnogr. Soc. Transform., vol. 3, no. 1, pp. 1-12, Jun. 2023, doi: 10.30631/DEMOS.V3I1.1812.
N. Ulinnuha and R. Veriani, “Analisis Cluster dalam Pengelompokan Provinsi di Indonesia Berdasarkan Variabel Penyakit Menular Menggunakan Metode Complete Linkage, Average Linkage dan Ward,” InfoTekJar J. Nas. Inform. dan Teknol. Jar., vol. 5, no. 1, pp. 102–108, Sep. 2020, doi: 10.30743/INFOTEKJAR.V5I1.2464.
S. F. Mu’afa and N. Ulinnuha, “Perbandingan Metode Single Linkage, Complete Linkage Dan Average Linkage dalam Pengelompokan Kecamatan Berdasarkan Variabel Jenis Ternak Kabupaten Sidoarjo,” Inf. J. Ilm. Bid. Teknol. Inf. dan Komun., vol. 4, no. 2, 2019, doi: 10.25139/inform.v4i2.1696.
A. L. Scutariu, Stefanita Susu, C. E. Huidumac-Petrescu, and R. M. Gogonea, “A Cluster Analysis Concerning the Behavior of Enterprises with E-Commerce Activity in the Context of the COVID-19 Pandemic,” J. Theor. Appl. Electron. Commer. Res. 2022, Vol. 17, Pages 47-68, vol. 17, no. 1, pp. 47-68, Dec. 2021, doi: 10.3390/JTAER17010003.
P. Giordani, M. B. Ferraro, and F. Martella, “An Introduction to Clustering with R,” vol. 1, 2020, doi: 10.1007/978-981-13-0553-5.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 bit-Tech : Binary Digital - Technology
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
I hereby assign and transfer to bit-Tech all exclusive copyright ownership rights to the above work. This includes, but is not limited to, the right to publish, republish, downgrade, distribute, transmit, sell, or use the work and other related materials worldwide, in whole, or in part, in all languages, in electronic, printed, or any other form of media, now known or hereafter developed and reserves the right to permit or license a third party to do any of the above. I understand that this exclusive right will belong to bit-Tech from the date the article is accepted for publication. I also understand that bit-Tech, as the copyright owner, has sole authority to license and permit reproduction of the article. I understand that, except for copyright, any other proprietary rights associated with the work (e.g. patents or other rights to any process or procedure) must be retained by the author. In addition, I understand that bit-Tech permits authors to use their papers in any way permitted by the applied Creative Commons license.