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Journal articles on the topic 'Subtractive clustering'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Haryati, Anisa Eka, and Sugiyarto Surono. "COMPARATIVE STUDY OF DISTANCE MEASURES ON FUZZY SUBTRACTIVE CLUSTERING." MEDIA STATISTIKA 14, no. 2 (2021): 137–45. http://dx.doi.org/10.14710/medstat.14.2.137-145.

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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 M

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2

Tongbram, Simon, Benjamin A. Shimray, and Loitongbam Surajkumar Singh. "Segmentation of image based on k-means and modified subtractive clustering." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 3 (2021): 1396–403. https://doi.org/10.11591/ijeecs.v22.i3.pp1396-1403.

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Image segmentation has widespread applications in medical science, for example, classification of different tissues, identification of tumors, estimation of tumor size, surgery planning, and atlas matching. Clustering is a widely implemented unsupervised technique used for image segmentation mainly because of its simplicity and fast computation. However, the quality and efficiency of clustering-based segmentation is highly depended on the initial value of the cluster centroid. In this paper, a new hybrid segmentation approach based on k-means clustering and modified subtractive clustering is p

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3

Yu, Li Zhe, Tiao Juan Ren, Zhang Quan Wang, and Ban Teng Liu. "Research on Vehicle Networking Clustering Routing Algorithm Based on Subtractive Clustering." Applied Mechanics and Materials 644-650 (September 2014): 2366–69. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.2366.

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Vehicle networking is an important part of intelligent traffic system. It combines wireless sensor network and mobile autonomous network, which is better for drivers to obtain road condition information to ensure driving safety. This paper focuses on the analysis of subtractive clustering and proposed clustering routing algorithm based on subtractive clustering in order to reduce communication of wireless sensor network and redundant flooding and routing expanse. In the algorithm, cluster head selection adopts subtractive clustering to produce cluster node in node intensive place. Cluster form

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4

Yang, Xiao Bo. "Evaluate Fabric Wrinkle Grade Based on Subtractive Clustering Adaptive Network Fuzzy Inference Systems." Advanced Materials Research 332-334 (September 2011): 1505–10. http://dx.doi.org/10.4028/www.scientific.net/amr.332-334.1505.

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In this paper, a new method of subtractive clustering adaptive network fuzzy inference systems is proposed to assess degree of wrinkle in the fabric. The clustering center can be gotten through subtractive clustering algorithm, which is the base to set up adaptive network inference systems. Firstly, subtractive clustering algorithm is used to confirm the structure of fuzzy neural network, then, fuzzy inference system is used to process pattern recognition. Finally, four kinds of fabric wrinkle feature parameters are used to verify the results on real fabric. The results show the applicability

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Tongbram, Simon, Benjamin A. Shimray, and Loitongbam Surajkumar Singh. "Segmentation of image based on k-means and modified subtractive clustering." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 3 (2021): 1396. http://dx.doi.org/10.11591/ijeecs.v22.i3.pp1396-1403.

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Image segmentation has widespread applications in medical science, for example, classification of different tissues, identification of tumors, estimation of tumor size, surgery planning, and atlas matching. Clustering is a widely implemented unsupervised technique used for image segmentation mainly because of its simplicity and fast computation. However, the quality and efficiency of clustering-based segmentation is highly depended on the initial value of the cluster centroid. In this paper, a new hybrid segmentation approach based on k-means clustering and modified subtractive clustering is p

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6

Christyanti, Ratna Dwi, Dady Sulaiman, Adymas Putro Utomo, and Muhammad Ayyub. "Clustering Wilayah Kerawanan Stunting Menggunakan Metode Fuzzy Subtractive Clustering." Jurnal Ilmiah Teknologi Informasi Asia 17, no. 1 (2022): 1. http://dx.doi.org/10.32815/jitika.v17i1.877.

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7

Kim, Dae-Won, KiYoung Lee, Doheon Lee, and Kwang H. Lee. "A kernel-based subtractive clustering method." Pattern Recognition Letters 26, no. 7 (2005): 879–91. http://dx.doi.org/10.1016/j.patrec.2004.10.001.

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8

Chen, JunYing, Zheng Qin, and Ji Jia. "A Weighted Mean Subtractive Clustering Algorithm." Information Technology Journal 7, no. 2 (2008): 356–60. http://dx.doi.org/10.3923/itj.2008.356.360.

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9

Farias, Marcos Santana, Nadia Nedjah, and Luiza De Macedo Mourelle. "A hardware architecture for subtractive clustering." International Journal of High Performance Systems Architecture 3, no. 2/3 (2011): 167. http://dx.doi.org/10.1504/ijhpsa.2011.040469.

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10

Dong, Rui, and Min Xiang Huang. "An Improved FCM Algorithm Based on Subtractive Clustering for Power Load Classification." Advanced Materials Research 986-987 (July 2014): 206–10. http://dx.doi.org/10.4028/www.scientific.net/amr.986-987.206.

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FCM is used in many power load classification currently, but it also has some shortcomings. This paper give an algorithm based on Subtractive Clustering and improved Fuzzy C-means Clustering (SUB-FCM) to solve this problem. This algorithm use subtractive clustering to initialize the cluster center matrix, solve the random initialization of FCM, and improve the global search ability, avoid falling into local optima. Experimental analysis found this algorithm also could accelerate the convergence speed, and has better clustering results. It can be applied to power load classification effectively

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11

Liu, Ban Teng, Ping Jie Huang, and Guang Xin Zhang. "The Applied Research of System Subtractive Clustering RBF Algorithm in Eddy Current Testing on the Conductive Structure Defect." Advanced Materials Research 712-715 (June 2013): 2030–34. http://dx.doi.org/10.4028/www.scientific.net/amr.712-715.2030.

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According the problem of defect type discrimination and quantitative detection of defect depth in eddy current testing (ECT) on the conductive structure defect. The text proposes a RBF optimization algorithm based on system subtractive clustering (SISCA),first of all, according to the likelihood of the data ,it uses the system clustering method to estimate the number of clustering center, and improves mathematical model of subtractive clustering to determine the clustering scheme, then takes the minimum of the largest distance variance in the cluster as evaluation index to obtain the optimal c

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12

Anggraini, Selfina. "Comparison of UMTS900 Node B Placement on Existing BTS Using Fuzzy C-Means and Fuzzy Subtractive Clustering." Jurnal Jartel: Jurnal Jaringan Telekomunikasi 4, no. 1 (2017): 44–52. http://dx.doi.org/10.33795/jartel.v4i1.193.

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This study conducted a comparison of the planning for the placement of UMTS900 Node B network on the existing BTS of one operator in the Malang area using the Fuzzy C-Means method. and Fuzzy Subtractive Clustering, which is expected to reduce the number of BTS towers and increase cost efficiency. So that we can find the appropriate method for placing Node B in order spread evenly from 5 districts in Malang City. The results showed that planning for the placement of Node B UMTS900 in 2019 required 15 Node B for urban areas and 2 for sub-urban areas spread over 5 sub-districts in Malang city. No

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13

El-Tarabily, Mariam, Rehab Abdel-Kader, Mahmoud Marie, and Gamal Abdel-Azeem. "A PSO-Based Subtractive Data Clustering Algorithm." International Journal of Research in Computer Science 3, no. 2 (2013): 1–9. http://dx.doi.org/10.7815/ijorcs.32.2013.060.

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14

Sun, Zhi Hai, Bin Hu, Ying Meng, and Wen Hui Zhou. "Tracking of Moving Objects in Video Sequences Based on Elliptical Subtractive Clustering." Applied Mechanics and Materials 734 (February 2015): 600–603. http://dx.doi.org/10.4028/www.scientific.net/amm.734.600.

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Visual object detection and tracking have become an important step between computer vision and video analysis. Recent methods almost use mean shift for tracking problems, which are difficult to overcome the shortcoming with the initial object model. Initial object model is almost initialized manually by user, which is not smart enough and inconvenient. This paper considers the integration strategy for elliptical subtractive clustering and mean shift, and proposes a novel tracking method based on elliptical subtractive clustering.

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15

Chen, Zhijia, Yuanchang Zhu, Yanqiang Di, and Shaochong Feng. "Self-Adaptive Prediction of Cloud Resource Demands Using Ensemble Model and Subtractive-Fuzzy Clustering Based Fuzzy Neural Network." Computational Intelligence and Neuroscience 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/919805.

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In IaaS (infrastructure as a service) cloud environment, users are provisioned with virtual machines (VMs). To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN). We analyze the characters of user preferences and demands. Then the architecture of the prediction model is constructed. We adopt some base predictors to compose the ensemble model. Then the structure and learning al

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16

Dhanachandra, Nameirakpam, Khumanthem Manglem, and Yambem Jina Chanu. "Image Segmentation Using K -means Clustering Algorithm and Subtractive Clustering Algorithm." Procedia Computer Science 54 (2015): 764–71. http://dx.doi.org/10.1016/j.procs.2015.06.090.

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17

Supartha, I. Kadek Dwi Gandika, and Adi Panca Saputra Iskandar. "Analisis Kinerja Fuzzy C-Means (FCM) dan Fuzzy Subtractive (FS) dalam Clustering Data Alumni STMIK STIKOM Indonesia." INFORMAL: Informatics Journal 6, no. 1 (2021): 41. http://dx.doi.org/10.19184/isj.v6i1.22077.

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In this study, clustering data on STMIK STIKOM Indonesia alumni using the Fuzzy C-Means and Fuzzy Subtractive methods. The method used to test the validity of the cluster is the Modified Partition Coefficient (MPC) and Classification Entropy (CE) index. Clustering is carried out with the aim of finding hidden patterns or information from a fairly large data set, considering that so far the alumni data at STMIK STIKOM Indonesia have not undergone a data mining process. The results of measuring cluster validity using the Modified Partition Coefficient (MPC) and Classification Entropy (CE) index,

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18

Banteng, Liu, Haibo Yang, Qiuxia Chen, and Zhangquan Wang. "Research on the subtractive clustering algorithm for mobile ad hoc network based on the Akaike information criterion." International Journal of Distributed Sensor Networks 15, no. 9 (2019): 155014771987761. http://dx.doi.org/10.1177/1550147719877612.

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Large and dense mobile ad hoc networks often meet scalability problems, the hierarchical structures are needed to achieve performance of network such as cluster control structure. Clustering in mobile ad hoc networks is an organization method dividing the nodes in groups, which are managed by the nodes called cluster-heads. As far as we know, the difficulty of clustering algorithm lies in determining the number and positions of cluster-heads. In this article, the subtractive clustering algorithm based on the Akaike information criterion is proposed. First, Akaike information criterion is intro

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19

Suerni, Widya, Memi Nor Hayati, and Rito Goejantoro. "A PENERAPAN METODE SUBTRACTIVE FUZZY C-MEANS PADA TINGKAT PARTISIPASI PENDIDIKAN JENJANG SEKOLAH MENENGAH ATAS/SEDERAJAT DI KABUPATEN/KOTA PULAU KALIMANTAN TAHUN 2018." VARIANCE: Journal of Statistics and Its Applications 2, no. 2 (2021): 63–74. http://dx.doi.org/10.30598/variancevol2iss2page63-74.

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Cluster analysis is a data exploration method uses to obtain hidden characteristics by forming data clusters. One of the cluster analysis methods is Subtractive Fuzzy C-Means (SFCM). SFCM is a combination of Subtractive Clustering and Fuzzy C-Means methods. The SFCM method has the advantages of not requiring many iterations and the results obtained are more stable and accurate than the FCM and SC methods. This study aims to determine the result of clustering on the enrollment rate data for Senior High School (SHS) / equivalent. The data used were the enrollment rate data for high school / equi

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20

Marzouk, Mohamed, and Mohamed Alaraby. "PREDICTING TELECOMMUNICATION TOWER COSTS USING FUZZY SUBTRACTIVE CLUSTERING." Journal of Civil Engineering and Management 21, no. 1 (2014): 67–74. http://dx.doi.org/10.3846/13923730.2013.802736.

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This paper presents a fuzzy subtractive modelling technique to predict the weight of telecommunication towers which is used to estimate their respective costs. This is implemented through the utilization of data from previously installed telecommunication towers considering four input parameters: a) tower height; b) allowed tilt or deflection; c) antenna subjected area loading; and d) wind load. Telecommunication towers are classified according to designated code (TIA-222-F and TIA-222-G standards) and structures type (Self-Supporting Tower (SST) and Roof Top (RT)). As such, four fuzzy subtrac

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21

Rao, U. Mohan, Y. R. Sood, and R. K. Jarial. "Subtractive Clustering Fuzzy Expert System for Engineering Applications." Procedia Computer Science 48 (2015): 77–83. http://dx.doi.org/10.1016/j.procs.2015.04.153.

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22

Santana Farias, Marcos, Nadia Nedjah, and Luiza de Macedo Mourelle. "Hardware implementation of subtractive clustering for radionuclide identification." Integration 46, no. 3 (2013): 220–29. http://dx.doi.org/10.1016/j.vlsi.2012.10.005.

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23

Casalino, Gabriella, Nicoletta Del Buono, and Corrado Mencar. "Subtractive clustering for seeding non-negative matrix factorizations." Information Sciences 257 (February 2014): 369–87. http://dx.doi.org/10.1016/j.ins.2013.05.038.

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24

Pal, Nikhil R., and Debrup Chakraborty. "Mountain and subtractive clustering method: Improvements and generalizations." International Journal of Intelligent Systems 15, no. 4 (2000): 329–41. http://dx.doi.org/10.1002/(sici)1098-111x(200004)15:4<329::aid-int5>3.0.co;2-9.

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25

Demirli, K., and P. Muthukumaran. "Higher order fuzzy system identification using subtractive clustering." Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology 9, no. 3-4 (2000): 129–58. https://doi.org/10.3233/ifs-2000-123.

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26

Cahyaningrum, P. Laras Sakti, and Nursyiva Irsalinda. "Indonesian community welfare levels clustering using the fuzzy subtractive clustering (FCM) method." Journal of Physics: Conference Series 1373 (November 2019): 012036. http://dx.doi.org/10.1088/1742-6596/1373/1/012036.

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27

SHIEH, HORNG-LIN, and CHENG-CHIEN KUO. "A NOVEL VALIDITY INDEX FOR THE SUBTRACTIVE CLUSTERING ALGORITHM." International Journal of Pattern Recognition and Artificial Intelligence 25, no. 04 (2011): 547–63. http://dx.doi.org/10.1142/s0218001411008798.

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This paper proposes a new validity index for the subtractive clustering (SC) algorithm. The subtractive clustering algorithm proposed by Chiu is an effective and simple method for identifying the cluster centers of sampling data based on the concept of a density function. The SC algorithm continually produces the cluster centers until the final potential compared with the original is less than a predefined threshold. The procedure is terminated when there are only a few data points around the most recent cluster. The choice of the threshold is an important factor affecting the clustering resul

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28

Cai, Yongchang. "Modeling for the Calcination Process of Industry Rotary Kiln Using ANFIS Coupled with a Novel Hybrid Clustering Algorithm." Mathematical Problems in Engineering 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/1067351.

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Rotary kiln is important equipment in heavy industries and its calcination process is the key impact to the product quality. Due to the difficulty in obtaining the accurate algebraic model of the calcination process, an intelligent modeling method based on ANFIS and clustering algorithms is studied. In the model, ANFIS is employed as the core structure, and aiming to improve both its performance in reduced computation and accuracy, a novel hybrid clustering algorithm is proposed by combining FCM and Subtractive methods. A quasi-random data set is then hired to test the new hybrid clustering al

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Raj, A. Stanley, D. Hudson Oliver, and Y. Srinivas. "Geoelectrical Data Inversion by Clustering Techniques of Fuzzy Logic to Estimate the Subsurface Layer Model." International Journal of Geophysics 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/134834.

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Soft computing based geoelectrical data inversion differs from conventional computing in fixing the uncertainty problems. It is tractable, robust, efficient, and inexpensive. In this paper, fuzzy logic clustering methods are used in the inversion of geoelectrical resistivity data. In order to characterize the subsurface features of the earth one should rely on the true field oriented data validation. This paper supports the field data obtained from the published results and also plays a crucial role in making an interdisciplinary approach to solve complex problems. Three clustering algorithms

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30

Salah, Halima, Mohamed Nemissi, Hamid Seridi, and Herman Akdag. "Subtractive Clustering and Particle Swarm Optimization Based Fuzzy Classifier." International Journal of Fuzzy System Applications 8, no. 3 (2019): 108–22. http://dx.doi.org/10.4018/ijfsa.2019070105.

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Setting a compact and accurate rule base constitutes the principal objective in designing fuzzy rule-based classifiers. In this regard, the authors propose a designing scheme based on the combination of the subtractive clustering (SC) and the particle swarm optimization (PSO). The main idea relies on the application of the SC on each class separately and with a different radius in order to generate regions that are more accurate, and to represent each region by a fuzzy rule. However, the number of rules is then affected by the radiuses, which are the main preset parameters of the SC. The PSO i

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31

Guan weihua, Li lifu, and Lin yongman. "Underdetermined Blind Separation Algorithm Base d on Subtractive Clustering." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 3, no. 7 (2011): 75–81. http://dx.doi.org/10.4156/aiss.vol3.issue7.9.

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32

Radionov, A. A., S. A. Evdokimov, A. A. Sarlybaev, and O. I. Karandaeva. "Application of Subtractive Clustering for Power Transformer Fault Diagnostics." Procedia Engineering 129 (2015): 22–28. http://dx.doi.org/10.1016/j.proeng.2015.12.003.

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33

Kokkinos, Y., and K. G. Margaritis. "Kernel averaged gradient descent subtractive clustering for exemplar selection." Evolving Systems 9, no. 4 (2017): 285–97. http://dx.doi.org/10.1007/s12530-017-9197-5.

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34

Torun, Yunis, and Gülay Tohumoğlu. "Designing simulated annealing and subtractive clustering based fuzzy classifier." Applied Soft Computing 11, no. 2 (2011): 2193–201. http://dx.doi.org/10.1016/j.asoc.2010.07.020.

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35

Shieh, Horng-Lin. "Robust validity index for a modified subtractive clustering algorithm." Applied Soft Computing 22 (September 2014): 47–59. http://dx.doi.org/10.1016/j.asoc.2014.05.001.

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36

Lin, Yong Man, Zi Ping Feng, and Hai Feng Guan. "Study on LPG Air Fuel Ratio Based on Improved Subtractive Clustering RBF Neural Networks." Key Engineering Materials 474-476 (April 2011): 1122–27. http://dx.doi.org/10.4028/www.scientific.net/kem.474-476.1122.

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The paper refers to the mathematical model of gasoline engine, and builds liquid-jet LPG(Liquefied Petroleum Gas) engine model. Based on the model, when the specific of the parameters distribution of operating engine are known, RBF neural network can estimate center value and the number of hidden layers precisely, and control engine A/F in fine range. But the parameter features of operating engine are unknown in advance. The paper provides a improved subtractive clustering - RBF neural Networks algorithm to control A/F of LPG engine. Simulation shows, improved subtractive clustering can precis

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37

Basbous, Raed. "Fuzzy Models for Short Term Power Forecasting in Palestine." Qubahan Academic Journal 3, no. 4 (2023): 361–73. http://dx.doi.org/10.58429/qaj.v3n4a268.

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Short-Term Load Forecasting (STLF) is needed to efficiently manage the power systems. In this paper, two kinds of models that depend on the Fuzzy based techniques are developed to represent the STLF models in Palestine. Different types of these models have been developed using the available data sets that include the past electric load values and the climatic variables as inputs. It is shown that the climatic variables have a major effect on the predicted load. Various optimization techniques are used to develop the proposed models including hybrid and Backpropagation optimization techniques,

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38

Lisangan, Erick Alfons, Aina Musdholifah, and Sri Hartati. "Two Level Clustering for Quality Improvement using Fuzzy Subtractive Clustering and Self-Organizing Map." TELKOMNIKA Indonesian Journal of Electrical Engineering 15, no. 2 (2015): 373. http://dx.doi.org/10.11591/tijee.v15i2.1552.

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Recently, clustering algorithms combined conventional methods and artificial intelligence. FSC-SOM is designed to handle the problem of SOM, such as defining the number of clusters and initial value of neuron weights. FSC find the number of clusters and the cluster centers which become the parameter of SOM. FSC-SOM is expected to improve the quality of FSC since the determination of the cluster centers are processed twice i.e. searching for data with high density at FSC then updating the cluster centers at SOM. FSC-SOM was tested using 10 datasets that is measured with F-Measure, entropy, Silh

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Irwandi, Opim Salim Sitompul, and Rahmat Widia Sembiring. "Performance Analysis of Subtractive Clustering Algorithm in Determining the Number and Position of Cluster Centers." Randwick International of Social Science Journal 2, no. 2 (2021): 193–99. http://dx.doi.org/10.47175/rissj.v2i2.241.

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The basic concept of the subtractive clustering algorithm is to choose a data point that has the highest density (potential) in a space (variable) as the center of the cluster. The number and position of the cluster centers formed are influenced by the given radius (r) parameter value. If the radius value is very small, it will result in the neglect of potential data points around the center of the cluster. If the value of the radius parameter is too large, it increases the contribution of all potential data points, thereby canceling the effect of cluster density. The number of cluster centers

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40

Ma, C. Y., D. L. Zhang, Zhi Wang, G. X. Li, and J. J. Tang. "Study on ANFIS Application in Coal Mining Stray Current Security Prediction." Key Engineering Materials 426-427 (January 2010): 216–19. http://dx.doi.org/10.4028/www.scientific.net/kem.426-427.216.

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On basis of analyzing the principles and structure of adaptive neural fuzzy inference system (ANFIS), this thesis used subtractive clustering algorithm to get fuzzy inference rule numbers and confirm the network structure. In addition, the thesis built ANFIS model adapted to coal mining workface stray current security prediction. The model can do workface stray current security prediction by the easy measured parameters of non-production field. If the stray current exceeds standard, the system will alarm on time. Moreover, the thesis compared accuracy rate of the security prediction results un

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41

Wahyuning Astuti, Reny, Sukma Puspitorini, and Desti Trimas Tuti. "PENENTUAN PRODUK UNGGULAN ONLINE SHOP MENGGUNAKAN K-MEANS DAN SUBTRACTIVE CLUSTERING." JURNAL AKADEMIKA 11, no. 1 (2018): 29–36. https://doi.org/10.53564/akademika.v11i1.299.

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Clustering is a method to search and classify data that has similarity characteristics between one data with other data. This clustering implementation can be applied to various fields as an example in terms of determining best-selling products. One method of clustering is often used because of its relatively quick and adaptable is the K-Means algorithm. Technique of grouping K-means method is very needed by Nasa Jambionline shop to classify their products. During this time Nasa Jambi online shop classifies the product by way of checking through sales memos and report books to find out and cal

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42

Wakhidah, Nur. "KLASIFIKASI DATA MULTIDIMENSI MENGGUNAKAN SUBTRACTIVE CLUSTERING DAN K-NEAREST NEIGHTBOR." Jurnal Transformatika 10, no. 1 (2012): 11. http://dx.doi.org/10.26623/transformatika.v10i1.65.

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<span>Proses pengelompokan data (</span><em>clustering</em><span>) pada data multi dimensi dapat dilakukan dengan menggunakan algoritma </span><em>Subtractive</em><em>Clustering</em><span>. Dalam proses pengelompokan data (</span><em>clustering</em><span>) untuk menentukan pusat </span><em>cluster</em><span> </span><em>(centroid</em><span>), dipengaruhi oleh besarnya nilai parameter yang diantaranya </span><em>influence range, squash, accept_rati

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PAN, Tian-Hong, Zhen-Kuang XUE, and Shao-Yuan LI. "An Online Multi-model Identification Algorithm Based on Subtractive Clustering." Acta Automatica Sinica 35, no. 2 (2009): 220–24. http://dx.doi.org/10.3724/sp.j.1004.2009.00220.

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44

Sarimveis, Haralambos, Alex Alexandridis, and George Bafas. "NEURAL NETWORK MODEL IDENTIFICATION BASED ON THE SUBTRACTIVE CLUSTERING METHOD." IFAC Proceedings Volumes 35, no. 1 (2002): 349–54. http://dx.doi.org/10.3182/20020721-6-es-1901.00711.

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45

Demirli, K., S. X. Cheng, and P. Muthukumaran. "Subtractive clustering based modeling of job sequencing with parametric search." Fuzzy Sets and Systems 137, no. 2 (2003): 235–70. http://dx.doi.org/10.1016/s0165-0114(02)00364-0.

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46

Widodo, Imam Djati. "Fuzzy subtractive clustering based prediction model for brand association analysis." MATEC Web of Conferences 154 (2018): 01082. http://dx.doi.org/10.1051/matecconf/201815401082.

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The brand is one of the crucial elements that determine the success of a product. Consumers in determining the choice of a product will always consider product attributes (such as features, shape, and color), however consumers are also considering the brand. Brand will guide someone to associate a product with specific attributes and qualities. This study was designed to identify the product attributes and predict brand performance with those attributes. A survey was run to obtain the attributes affecting the brand. Subtractive Fuzzy Clustering was used to classify and predict product brand as

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Asaadian, Hamidreza, Hosein Zanbouri, and Bahram Soltani Soulgani. "Bitumen-water interfacial tension modeling by using subtractive clustering method." Petroleum Science and Technology 36, no. 11 (2018): 765–71. http://dx.doi.org/10.1080/10916466.2018.1446170.

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Goyal, Lalit Mohan, Mamta Mittal, and Jasleen Kaur Sethi. "Fuzzy model generation using Subtractive and Fuzzy C-Means clustering." CSI Transactions on ICT 4, no. 2-4 (2016): 129–33. http://dx.doi.org/10.1007/s40012-016-0090-3.

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Palla, Parasuram Yadav, Amba Shetty, B. S. Raghavendra, and A. V. Narasimhadhan. "Subtractive clustering and phase correlation similarity measure for endmember extraction." Infrared Physics & Technology 110 (November 2020): 103452. http://dx.doi.org/10.1016/j.infrared.2020.103452.

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DELIMAWATI, SRI, HAZMIRA YOZZA, and MAIYASTRI MAIYASTRI. "PENGELOMPOKAN KABUPATEN/KOTA DI SUMATERA BARAT BERDASARKAN FAKTOR TERKAIT KEJADIAN DEMAM BERDARAH DENGUE DENGAN METODE FUZZY SUBTRACTIVE CLUSTERING." Jurnal Matematika UNAND 10, no. 1 (2021): 150. http://dx.doi.org/10.25077/jmu.10.1.150-158.2021.

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. Demam berdarah dengue(DBD) adalah penyakit infeksi virus akut yang disebabkan oleh virus dengue. Provinsi Sumatera Barat merupakan salah satu provinsi di Indonesia yang memiliki angka penderita DBD cukup tinggi. Penelitian ini bertujuan untuk mengelompokkan kabupaten/kota di Sumatera Barat berdasarkan faktor terkait kejadian DBD yakni kejadian banjir, penampungan air, fasilitas dan tenaga kesehatan dan penduduk miskin. Metode yang digunakan dalam penelitian ini adalah metode fuzzy subtractive clustering(FSC). Berdasarkan pengolahan dengan metode FSC didapat hasil pengklasteran dengan 2 klast

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