K-MEANS AND AGGLOMERATIVE HIERARCHY CLUSTERING ANALYSIS ON THE STAINLESS STEEL CORROSION PROBLEM
Abstract
Stainless Steel (SS) is a material that is widely used in various fields because it is resistant to corrosion. However, if SS is exposed to heat at high temperatures for a long period of time, a sigma phase, namely the Fe-Cr compound, will form, which indicates that corrosion has begun. The appearance of this corrosion can be detected through color changes on the SS surface, ranging from light brown to dark blue. Corrosion events will be observed through the distribution of color on the sample surface at the location selected through the SS microstructure image. Cluster analysis will be used to group the colors on the surface of the SS sample through the images used. The results of cluster analysis can be used to identify SS color which indicates the appearance of corrosion in the sample. In this research, we will examine the determination of many clusters for K-Means and Agglomerative Hierarchy with Ward's Criterion, Single, Average, and Complete Linkages. In addition, the model quality measure was tested with Silhouette Coeficient. Single linkage gives the worst results because it gives the impression that only one dominant color appears so it can be said that it is unable to distribute each color to the specified cluster. Likewise with Average because the number of clusters cannot be determined with certainty. On the other hand, the K-Means results are similar to Ward's results, this is reasonable because the basic idea of both is to find the minimum distance between each object and its center, in this case the average is used as the measure of the center, while the results that are most similar to the original image are clustering uses complete linkage. These results can be used as recommendations for academics and practitioners in the fields of Statistics, Mathematics and Materials Engineering in the subsequent analysis process to solve SS corrosion problems.
Downloads
References
H. K. D. H. Bhadeshia, “Preface to the fourth edition,” Elsevier EBooks, 2017, doi: 10.1016/b978-0-08-100270-4.00023-8.
G. S. Da Fonseca, P. M. De Oliveira, M. G. Diniz, D. V. Bubnoff, and J. A. De Castro, “SIGMA phase in Superduplex Stainless Steel: formation, kinetics and microstructural path,” Mater. Res.-Ibero-Am. J. Mater., vol. 20, no. 1, pp. 249–255, Jan. 2017, doi: 10.1590/1980-5373-mr-2016-0436.
Y. S. Afrianti, H. Ardy, U. S. Pasaribu, and F. D. E. Latief, “Identification of Inhomogeneous Temperature on Stainless Steel using Statistical Analysis,” J. Phys., vol. 2084, no. 1, p. 012008, Nov. 2021, doi: 10.1088/1742-6596/2084/1/012008.
N. N. Almanza-Ortega et al., “Corrosion analysis through an adaptive preprocessing strategy using the K-Means algorithm,” Procedia Comput. Sci., vol. 219, pp. 586–595, Jan. 2023, doi: doi: 10.1016/j.procs.2023.01.327.
T. Gibbons, G. Pierce, K. Worden, and I. Antoniadou, “A Gaussian mixture model for automated corrosion detection in remanufacturing,” in A Gaussian mixture model for automated corrosion detection in remanufacturing, in Advances in Transdisciplinary Engineering, vol. 8. University of Skövde, Sweden: IOP Press, Sep. 2018, pp. 63–68. doi: https://doi.org/10.3233/978-1-61499-902-7-63.
M. Darwis, L. H. Hasibuan, M. B. Firmansyah, N. Ahady, and R. Tiaharyadini, “Implementation of K-Means clustering algorithm in mapping the groups of graduated or dropped-out students in the Management Department of the National University,” JISA J. Inform. Dan Sains, vol. 4, no. 1, pp. 1–9, Jun. 2021, doi: 10.31326/jisa.v4i1.848.
I. B. G. Sarasvananda, R. Wardoyo, and A. K. Sari, “The K-Means clustering algorithm with semantic similarity to estimate the cost of hospitalization,” IJCCS, vol. 13, no. 4, p. 313, Oct. 2019, doi: 10.22146/ijccs.45093.
V. Nellie, V. C. Mawardi, and N. J. Perdana, “IMPLEMENTASI METODE AGGLOMERATIVE HIERARCHICAL CLUSTERING UNTUK SISTEM REKOMENDASI FILM,” E-J. Ilmu Komput. Dan Sist. Inf., vol. 11, no. 1, Jun. 2023, doi: 10.24912/jiksi.v11i1.24070.
R. Kusumastuti, E. Bayunanda, A. M. Rifa’i, M. R. G. Asgar, F. I. Ilmawati, and K. Kusrini, “Clustering titik panas menggunakan algoritma Agglomerative Hierarchical Clustering (AHC),” Cogito Smart J., vol. 8, no. 2, pp. 501–513, Dec. 2022, doi: 10.31154/cogito.v8i2.438.501-513.
R. E. Van Ruitenbeek, G. Koole, and S. Bhulai, “A hierarchical agglomerative clustering for product sales forecasting,” Decis. Anal. J., vol. 8, p. 100318, Sep. 2023, doi: 10.1016/j.dajour.2023.100318.
S. Sinharay, “An overview of statistics in education,” Elsevier EBooks, pp. 1–11, 2010, doi: 10.1016/b978-0-08-044894-7.01719-x.
“Analisis Cluster,” Universitas pendidikan Ganesha. Accessed: Aug. 24, 2023. [Online]. Available: https://cdn.undiksha.ac.id/wp-content/uploads/sites/10/2019/06/19222821/analisis-kluster.pdf
K. M. A. Patel and P. Thakral, “The best clustering algorithms in data mining,” presented at the 2016 International Conference on Communication and Signal Processing (ICCSP), Melmaruvathur, India, pp. 2042–2046. doi: 10.1109/ICCSP.2016.7754534.
A. M. Ikotun, A. E. Ezugwu, L. Abualigah, B. Abuhaija, and H. Jia, “K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data,” Inf. Sci., vol. 622, pp. 178–210, Apr. 2023, doi: 10.1016/j.ins.2022.11.139.
J. Pérez-Ortega, N. N. Almanza-Ortega, A. Vega-Villalobos, R. Pazos-Rangel, C. Zavala-Díaz, and A. M. Rebollar, “The K-Means algorithm evolution,” IntechOpen EBooks, 2020, doi: 10.5772/intechopen.85447.
Google Machine Learning Courses, “k-Means Advantages and Disadvantages,” Google Machine Learning.
N. Xu, R. B. Finkelman, S. Dai, C. Xu, and M. Peng, “Average Linkage Hierarchical Clustering Algorithm for Determining the Relationships between Elements in Coal,” ACS Omega, vol. 6, no. 9, pp. 6206–6217, Feb. 2021, doi: 10.1021/acsomega.0c05758.
P. Praveen, M. Kumar, M. A. Shaik, R. Ravikumar, and R. Kiran, “The comparative study on agglomerative hierarchical clustering using numerical data,” IOP Conf. Ser., vol. 981, no. 2, p. 022071, Dec. 2020, doi: 10.1088/1757-899x/981/2/022071.
L. R. Emmendorfer and A. M. P. Canuto, “A generalized average linkage criterion for Hierarchical Agglomerative Clustering,” Appl. Soft Comput., vol. 100, p. 106990, Mar. 2021, doi: 10.1016/j.asoc.2020.106990.
S. T. Ahmed, S. S. Kumar, B. Anusha, P. Bhumika, M. Gunashree, and B. Ishwarya, “A Generalized Study on Data Mining and Clustering Algorithms,” Springer EBooks, pp. 1121–1129, Jan. 2020, doi: 10.1007/978-3-030-41862-5_114.
K. K. Mohbey and G. S. Thakur, “An experimental survey on single linkage clustering,” Int. J. Comput. Appl., vol. 76, no. 17, pp. 6–11, Aug. 2013, doi: 10.5120/13337-0327.
Y. Rani and H. Rohil, “A Study of Hierarchical Clustering Algorithm,” Int. J. Inf. Comput. Technol., vol. 3, no. 11, pp. 1225–1232, 2013.
C. Shalizi, “Distances between Clustering, Hierarchical Clustering.” Carnegie Mellon University, Sep. 14, 2009. [Online]. Available: https://www.stat.cmu.edu/~cshalizi/350/lectures/08/lecture-08.pdf
T. Li, A. Rezaeipanah, and E. M. T. E. Din, “An ensemble agglomerative hierarchical clustering algorithm based on clusters clustering technique and the novel similarity measurement,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 6, pp. 3828–3842, Jun. 2022, doi: doi: 10.1016/j.jksuci.2022.04.010.
Vijaya, S. Sharma, and N. Batra, “Comparative Study of Single Linkage, Complete Linkage, and Ward Method of Agglomerative Clustering,” presented at the 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), Faridabad, India, 2019, pp. 568–573. doi: 10.1109/COMITCon.2019.8862232.
J. Gao, “Clustering Lecture 3: Hierarchical Methods.”
S. Patel, S. Sihmar, and A. Jatain, “A study of hierarchical clustering algorithms,” presented at the International Conference on Computing for Sustainable Global Development, Mar. 2015, pp. 537–541. [Online]. Available: http://ieeexplore.ieee.org/document/7100308.
Copyright (c) 2024 Yuli Sri Afrianti, Udjianna Sekteria Pasaribu, Fadhil Hanif Sulaiman, Grace Angelia, Henry Junus Wattimanela
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this Journal agree to the following terms:
- Author retain copyright and grant the journal right of first publication with the work simultaneously licensed under a creative commons attribution license that allow others to share the work within an acknowledgement of the work’s authorship and initial publication of this journal.
- Authors are able to enter into separate, additional contractual arrangement for the non-exclusive distribution of the journal’s published version of the work (e.g. acknowledgement of its initial publication in this journal).
- Authors are permitted and encouraged to post their work online (e.g. in institutional repositories or on their websites) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published works.
