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(Universitas Ahmad Dahlan Yogyakarta, Indonesia)(2) * sugiyarto - surono
(Universitas Ahmad Dahlan Yogyakarta, Indonesia)(3) Tommy Tanu Wijaya
(School of Mathematical Sciences, Beijing Normal University, China, China)(4) Goh Khang Wen
(INTI International and Colleges Malaysia, Malaysia)(5) Aris Thobirin
(Matematika Universitas Ahmad Dahlan, Indonesia)*corresponding author
AbstractObjective: Fuzzy clustering algorithm is a partition method used to assign objects from a data set to a cluster by marking the average location. Furthermore, Fuzzy Subtractive Clustering (FSC) with hamming distance and exponential membership function is used to analyze the cluster center of a data point. Therefore, the purpose of this research is to determine the number of clusters with the best quality by comparing the Partition Coefficient (PC) values for each number produced. Methods: The data set which is heart failure patient data is 150 data obtained from UCI Machine Learning. The data consists of 11 variables, including age , anemia , creatinine phosphokinase , diabetes ejection fraction , high blood pressure , platelets , serum creatinine , serum sodium , gender , and smoke . It simulated and processed using Fuzzy Subtractive Clustering Algorithm, Jupyter Notebook Software with Python programming language. Result: The results showed that the most optimal number of clusters is 3, which are selected based on the largest PC value. Conclusion: Based on the results obtained, the highest P value is in cluster 3, therefore heart failure can be grouped into 3, namely low, moderate, severe. Keywordsclustering; fuzzy subtractive clustering; Hamming distance; Exponential membership function
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DOIhttps://doi.org/10.29099/ijair.v7i1.306 |
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References
I. Sangadji, Y. Arvio, and Indrianto, “Dynamic segmentation of behavior patterns based on quantity value movement using fuzzy subtractive clustering method,” J. Phys. Conf. Ser., vol. 974, no. 1, pp. 0–7, 2018, doi: 10.1088/1742-6596/974/1/012009.
C. Gu and K. F. Kim, “Fuzzy clustering algorithm of interactive multi-sensor probabilistic data,” J. Intell. Fuzzy Syst., vol. 35, no. 4, pp. 4267–4275, 2018, doi: 10.3233/JIFS-169747.
A. C. Rencher, Methods of multivariate analysis, Second edi., vol. 4, no. 1. United States of America: John wiley & sons, inc., 2016.
C. C. Aggarwal, An Introduction to Cluster Analysis, 1 st. 2014.
Rahmawati, Wartono, and A. Y. Putri, “Pengklasteran tingkat pendidikan pegawai menggunakan metode fuzzy substractive clustering ( studi kasus : badan kepegawaian dan pelatihan daerah provinsi Riau),” vol. 5, no. 1, pp. 79–89, 2019.
R. Sharma and K. Verma, “Fuzzy shared nearest neighbor clustering,” Int. J. Fuzzy Syst., vol. 21, no. 8, pp. 2667–2678, 2019, doi: 10.1007/s40815-019-00699-7.
K. Sun, L. Liu, J. Qiu, and G. Feng, “Fuzzy Adaptive Finite-Time Fault-Tolerant Control for Strict-Feedback Nonlinear Systems,” IEEE Trans. Fuzzy Syst., vol. PP, no. c, pp. 1–10, 2020, doi: 10.1109/tfuzz.2020.2965890.
S. Kangkang, Q. Jianbin, H. R. Karimi, and Y. Fu, “Event-Triggered Robust Fuzzy Adaptive Finite-Time Control of Nonlinear Systems with Prescribed Performance,” IEEE Trans. Fuzzy Syst., vol. 6706, no. c, pp. 1–12, 2020, doi: 10.1109/tfuzz.2020.2979129.
E. Mehdizadeh, S. Nezhad, Sadi, S, and R. Moghhaddam, Tavakkoli, “Optimization of fuzzy clustering criteria by a hybrid pso and fuzzy c-means clustering algorithm,” vol. 5, no. 3, pp. 1–14, 2008.
S. Surono, A. E. Haryati, and J. Eliyanto, An Optimization of Several Distance Function on Fuzzy Subtractive Clustering. IOS Press, 2021.
Y. Syahra and M. Syahril, “Implementasi data mining dengan menggunakan algoritma fuzzy subtractive clustering dalam pengelompokan nilai untuk menentukan minat belajar siswa smp primbana Medan,” vol. 17, no. 1, pp. 54–63, 2018.
A. E. Haryati and Sugiyarto, “Clustering with Principal Component Analysis and Fuzzy Subtractive Clustering Using Membership Function Exponential and Hamming Distance,” in IOP Conference Series: Materials Science and Engineering, 2021, vol. 1077, no. 1, p. 012019, doi: 10.1088/1757-899x/1077/1/012019.
S. Surono and R. D. A. Putri, “Optimization of fuzzy c-means clustering algorithm with combination of minkowski and chebyshev distance using principal component analysis,” Int. J. Fuzzy Syst., 2020, doi: 10.1007/s40815-020-00997-5.
O. Rodrigues, “Combining minkowski and cheyshev: new distance proposal and survey of distance metrics using k-nearest neighbours classifier,” Pattern Recognit. Lett., vol. 110, pp. 66–71, 2018, doi: 10.1016/j.patrec.2018.03.021.
H. Hosseini, B. Tousi, and N. Razmjooy, “Application of fuzzy subtractive clustering for optimal transient performance of automatic generation control in restructured power system,” J. Intell. Fuzzy Syst., vol. 26, no. 3, pp. 1155–1166, 2014, doi: 10.3233/IFS-130802.
L. M. Goyal, M. Mittal, and J. K. Sethi, “Fuzzy model generation using subtractive and fuzzy c-means clustering,” CSI Trans. ICT, vol. 4, no. 2–4, pp. 129–133, 2016, doi: 10.1007/s40012-016-0090-3.
X. Zhao and G. Yang, “An entropy-based online multi-model identification algorithm and generalized predictive control,” J. Intell. Fuzzy Syst., vol. 32, no. 3, pp. 2339–2349, 2017, doi: 10.3233/JIFS-16317.
L. G. Martínez, J. R. Castro, G. Licea, A. Rodríguez-Díaz, and R. Salas, “Implementing fuzzy subtractive clustering to build a personality fuzzy model based on big five patterns for engineers,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 8266 LNAI, no. PART 2, pp. 497–508, 2013, doi: 10.1007/978-3-642-45111-9_43.
L. T. Ngo and B. H. Pham, “Approach to image segmentation based on interval type-2 fuzzy subtractive clustering,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 7197 LNAI, no. PART 2, pp. 1–10, 2012, doi: 10.1007/978-3-642-28490-8_1.
T. Sonamani Singh, P. Verma, and R. D. S. Yadava, “Fuzzy subtractive clustering for polymer data mining for saw sensor array based electronic nose,” Adv. Intell. Syst. Comput., vol. 546, pp. 245–253, 2017, doi: 10.1007/978-981-10-3322-3_23.
S. Anggraini, M. Kusumawardani, and H. Darmono, “Perbandingan penempatan node b umts900 pada bts existing menggunakan fuzzy c-means dan fuzzy subtractive clustering,” J. JARTEL, vol. 4, no. 1, pp. 170–177, 2017.
S. Kusumadewi and H. Purnomo, Aplikasi logika fuzzy untuk pendukung keputusan. Yogyakarta: Graha Ilmu, 2010.
E. S. Abdolkarimi and M. R. Mosavi, “Wavelet-adaptive neural subtractive clustering fuzzy inference system to enhance low-cost and high-speed INS/GPS navigation system,” GPS Solut., vol. 24, no. 2, pp. 1–17, 2020, doi: 10.1007/s10291-020-0951-y.
K. Rezaei and H. Rezaei, “New distance and similarity measures for hesitant fuzzy sets and their application in hierarchical clustering,” J. Intell. Fuzzy Syst., vol. 39, no. 3, pp. 4349–4360, 2020, doi: 10.3233/JIFS-200364.
S. Mahajan and S. K. Gupta, “On fully intuitionistic fuzzy multiobjective transportation problems using different membership functions,” Ann. Oper. Res., 2019, doi: 10.1007/s10479-019-03318-8.
I. P. Debnath and S. K. Gupta, “Exponential membership function and duality gaps for i-fuzzy linear programming problems,” Iran. J. Fuzzy Syst., vol. 16, no. 2, pp. 147–163, 2019, doi: 10.22111/ijfs.2019.4549.
K. Rezaei and H. Rezaei, “New distance and dimilarity measures for hesitant fuzzy soft sets,” vol. 16, no. 6, pp. 159–176, 2019.
J. C. Bezdek, Pattern recognition with fuzzy objective function algorithms. New York: Plenum Press, 1981.
P. L. S. Cahyaningrum and N. Irsalinda, “Indonesian community welfare levels clustering using the fuzzy subtractive clustering (FCM) method,” J. Phys. Conf. Ser., vol. 1373, no. 1, 2019, doi: 10.1088/1742-6596/1373/1/012036.
N. Azizah, D. Yuniarti, and R. Goejantoro, “Penerapan Metode Fuzzy Subtractive Clustering (Studi Kasus: Pengelompokkan Kecamatan di Provinsi Kalimantan Timur Berdasarkan Luas Daerah dan Jumlah Penduduk Tahun 2015),” J. EKSPONENSIAL, vol. 9, no. 2, pp. 197–206, 2018.
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