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Anisa Eka Haryati  -  Magister Pendidikan Matematika, Universitas Ahmad Dahlan, Indonesia
*Sugiyarto Surono orcid publons  -  Department Mathematic, Universitas Ahmad Dahlan, Indonesia
Open Access Copyright (c) 2021 MEDIA STATISTIKA under

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Clustering is a data analysis process which applied to classify the unlabeled data. Fuzzy clustering is a clustering method based on membership value which enclosing set of fuzzy as a measurement base for classification process. Fuzzy Subtractive Clustering (FSC) is included in one of fuzzy clustering method. This research applies Hamming distance and combined Minkowski Chebysev distance as a distance parameter in Fuzzy Subtractive Clustering. The objective of this research is to compare the output quality of the cluster from Fuzzy Subtractive Clustering by using Hamming distance and combine Minkowski Chebysev distance. The comparison of the two distances aims to see how well the clusters are produced from two different distances. The data used is data on hypertension. The variables used are age, gender, systolic pressure, diastolic pressure, and body weight. This research shows that the Partition Coefficient value resulted on Fuzzy Subtractive Clustering by applying combined Minkowski Chebysev distance is higher than the application of Hamming distance. Based on this, it can be concluded that in this study the quality of the cluster output using the combined Minkowski Chebysev distance is better.
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Keywords: Fuzzy Subtractive Clustering; Hamming; Combination of Minkowski Chebysev
Funding: -

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  1. Abdolkarimi, E. S., & Mosavi, M. R. (2020). Wavelet-adaptive neural subtractive clustering fuzzy inference system to enhance low-cost and high-speed INS/GPS navigation system. GPS Solutions, 24(2), 1–17.
  2. Banteng, L., Yang, H., Chen, Q., & Wang, Z. (2019). Research on the subtractive clustering algorithm for mobile ad hoc network based on the akaike information criterion. International Journal of Distributed Sensor Networks, 15(9).
  3. Benmouiza, K., & Cheknane, A. (2019). Clustered ANFIS network using fuzzy c-means, subtractive clustering, and grid partitioning for hourly solar radiation forecasting. Theoretical and Applied Climatology, 137(1–2), 31–43.
  4. Chen, C., & Deng, X. (2020). Several new results based on the study of distance measures of intuitionistic fuzzy sets. Iranian Journal of Fuzzy Systems, 17(2), 147–163.
  5. Debnath, I. P., & Gupta, S. K. (2019). Exponential membership function and duality gaps for i-fuzzy linear programming problems. Iranian Journal of Fuzzy Systems, 16(2), 147–163.
  6. Dyvak, M., Maslyiak, Y., Voytyuk, I., & Maslyiak, B. (2018). Modified method of subtractive clustering for modeling of distribution of harmful vehicles emission concentrations. CEUR Workshop Proceedings, 2300, 58–62
  7. Gan, G., Ma, C., & Wu, J. (2007). Data clustering: theory, algorithms, and applications. In Data Clustering: Theory, Algorithms, and Applications (Issue January 2007).
  8. Ghane’i Ostad, M., Vahdat Nejad, H., & Abdolrazzagh Nezhad, M. (2018). Detecting overlapping communities in LBSNs by fuzzy subtractive clustering. Social Network Analysis and Mining, 8(1), 1–11.
  9. Jang, J. S. R., Sun, C. T., & Mizutani, E. (2005). Neuro-fuzzy and soft computing-a computational approach to learning and machine intelligence. In IEEE Transactions on Automatic Control (Vol. 42, Issue 10).
  10. Mahajan, S., & Gupta, S. K. (2019). On fully intuitionistic fuzzy multiobjective transportation problems using different membership functions. Annals of Operations Research.
  11. Rencher, A. C. (2016). Methods of multivariate analysis (Second edi, Vol. 4, Issue 1). John wiley & sons, inc
  12. Rezaei, K., & Rezaei, H. (2019). New distance and similarity measures for hesitant fuzzy soft sets. 16(6), 159–176
  13. Rodrigues, O. (2018). Combining minkowski and cheyshev: new distance proposal and survey of distance metrics using k-nearest neighbours classifier. Pattern Recognition Letters, 110, 66–71.
  14. Salah, H., Nemissi, M., Seridi, H., & Akdag, H. (2019). Subtractive clustering and particle swarm optimization based fuzzy classifier. International Journal of Fuzzy System Applications, 8(3), 108–122.
  15. Sangadji, I., Arvio, Y., & Indrianto. (2018). Dynamic segmentation of behavior patterns based on quantity value movement using fuzzy subtractive clustering method. Journal of Physics: Conference Series, 974(1), 0–7.
  16. Sharma, R., & Verma, K. (2019). Fuzzy shared nearest neighbor clustering. International Journal of Fuzzy Systems, 21(8), 2667–2678.
  17. Surono, S., & Putri, R. D. A. (2020). Optimization of fuzzy c-means clustering algorithm with combination of minkowski and chebyshev distance using principal component analysis. International Journal of Fuzzy Systems.
  18. Utomo, V., & Marutho, D. (2018). Measuring hybrid sc-fcm clustering with cluster validity index. 2018 International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2018, C, 322–326.
  19. Zeng, S., Chen, S. M., & Teng, M. O. (2019). Fuzzy forecasting based on linear combinations of independent variables, subtractive clustering algorithm and artificial bee colony algorithm. Information Sciences, 484, 350–366.

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