Relevant bibliographies by topics / Subtractive clustering

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Subtractive clustering'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Subtractive clustering"

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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Subtractive clustering"

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Keneni, Blen M. Keneni. "Evolving Rule Based Explainable Artificial Intelligence for Decision Support System of Unmanned Aerial Vehicles." University of Toledo / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1525094091882295.

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Farias, Marcos Santana. "Hardware reconfigurável para identificação de radionuclídeos utilizando o método de agrupamento subtrativo." Universidade do Estado do Rio de Janeiro, 2012. http://www.bdtd.uerj.br/tde_busca/arquivo.php?codArquivo=7451.

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Fontes radioativas possuem radionuclídeos. Um radionuclídeo é um átomo com um núcleo instável, ou seja, um núcleo caracterizado pelo excesso de energia que está disponível para ser emitida. Neste processo, o radionuclídeo sofre o decaimento radioativo e emite raios gama e partículas subatômicas, constituindo-se na radiação ionizante. Então, a radioatividade é a emissão espontânea de energia a partir de átomos instáveis. A identificação correta de radionuclídeos pode ser crucial para o planejamento de medidas de proteção, especialmente em situações de emergência, definindo o tipo de fonte de ra
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Traore, Oumar Issiaka. "Méthodologie de traitement et d'analyse de signaux expérimentaux d'émission acoustique : application au comportement d'un élément combustible en situation accidentelle." Thesis, Aix-Marseille, 2018. http://www.theses.fr/2018AIXM0011/document.

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L’objectif de cette thèse est de contribuer à l’amélioration du processus de dépouillement d’essais de sûreté visant étudier le comportement d'un combustible nucléaire en contexte d’accident d’injection de réactivité (RIA), via la technique de contrôle par émission acoustique. Il s’agit notamment d’identifier clairement les mécanismes physiques pouvant intervenir au cours des essais à travers leur signature acoustique. Dans un premier temps, au travers de calculs analytiques et des simulation numériques conduites au moyen d’une méthode d’éléments finis spectraux, l’impact du dispositif d’essai
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Lin, Bing-Hung, and 林秉鴻. "Identification of mutation hotspots using mountain subtractive clustering." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/11847752827941924353.

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Huang, Ying-Chen, and 黃盈禎. "Q-Motif: Phosphorylation Motif Finding by Subtractive Clustering." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/49083541375728612917.

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碩士<br>國立陽明大學<br>生物醫學資訊研究所<br>99<br>Development of computational algorithms to discover biologically relevant phosphorylation motifs is becoming more and more important with the rapid increase in the proteomic sequences. Here we present a novel unsupervised method, called Quick Motif Finder, (in short, Q-Motif) to extract phosphorylation motifs. Q-Motif adopts the subtractive clustering algorithm to discover motifs exploiting statistical information hidden in phosphorylated data. The phosphorylated data is clustered into homogeneous groups, which are analyzed to identify candidate motifs. These
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Yeh, Shu-Wen, and 葉紓彣. "Identification of clinically associated mutation cancer hotspots using mountain subtractive clustering." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/pze746.

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Wang, Jung-Jing, and 王中敬. "Analysis of ENG Signal from Primary Motor Cortex of Rats Using ICA and Subtractive Clustering." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/w2u56x.

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碩士<br>國立臺北科技大學<br>自動化科技研究所<br>96<br>In the study, we propose an effective method using ICA and subtractive clustering to analyze (Electroneurography) ENG signal of the rat. We have completed the system which can record 8-channel ENG signal. Using the 8-channel multi-electrode array and then implant it into the primary motor cortex of rats. The signal is amplified through front-stage amplitude circuit and after-stage amplitude band-pass filter circuit. And, we record the ENG signal into PC through A/D card. When recording the ENG signal, we also synchronously monitor the activity images of rats
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Book chapters on the topic "Subtractive clustering"

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Nedjah, Nadia, and Luiza de Macedo Mourelle. "A Reconfigurable Hardware for Subtractive Clustering." In Hardware for Soft Computing and Soft Computing for Hardware. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03110-1_7.

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Ngo, Long Thanh, and Binh Huy Pham. "A Type-2 Fuzzy Subtractive Clustering Algorithm." In Advances in Intelligent and Soft Computing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27329-2_54.

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Santana Farias, Marcos, Nadia Nedjah, and Luiza de Macedo Mourelle. "Reconfigurable Hardware to Radionuclide Identification Using Subtractive Clustering." In Algorithms and Architectures for Parallel Processing. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24669-2_37.

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Gu, Lei. "An Evolutionary Semi-Supervised Subtractive Clustering Method by Seeding." In Lecture Notes in Electrical Engineering. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34522-7_105.

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Casalino, Gabriella, Nicoletta Del Buono, and Corrado Mencar. "Subtractive Initialization of Nonnegative Matrix Factorizations for Document Clustering." In Fuzzy Logic and Applications. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23713-3_24.

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Zhu, Qi, Huifu Zhang, and Quanqin Yang. "Semi-supervised Affinity Propagation Clustering Based on Subtractive Clustering for Large-Scale Data Sets." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46248-5_32.

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Chen, JunYing, and Zheng Qin. "Training RBF Neural Networks with PSO and Improved Subtractive Clustering Algorithms." In Neural Information Processing. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11893257_125.

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Lee, Younjeong, Ki Yong Lee, and Jaeyeol Rheem. "Speaker Identification Based on Subtractive Clustering Algorithm with Estimating Number of Clusters." In Text, Speech and Dialogue. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11551874_32.

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Ngo, Long Thanh, and Binh Huy Pham. "Approach to Image Segmentation Based on Interval Type-2 Fuzzy Subtractive Clustering." In Intelligent Information and Database Systems. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28490-8_1.

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Hsieh, June-Nan, Mehboob Ali, and Miin-Shen Yang. "Subtractive Clustering for Categorical Data with a Novel Separation Difference Validity Index." In Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70665-4_184.

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Conference papers on the topic "Subtractive clustering"

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Muhammad, Mohd Nazrin, Zoran Salcic, and Kevin I.-Kai Wang. "Subtractive Clustering as ZUPT Detector." In 2014 IEEE 11th Intl Conf on Ubiquitous Intelligence & Computing and 2014 IEEE 11th Intl Conf on Autonomic & Trusted Computing and 2014 IEEE 14th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom). IEEE, 2014. http://dx.doi.org/10.1109/uic-atc-scalcom.2014.114.

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Gu, Lei. "Subtractive clustering for categorical data." In 2016 12th International Conference on Natural Computation and 13th Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). IEEE, 2016. http://dx.doi.org/10.1109/fskd.2016.7603354.

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Wang, A. C., and B. J. Jeng. "Subtractive clustering for PCA image coding." In 2013 2nd International Symposium on Next-Generation Electronics (ISNE 2013). IEEE, 2013. http://dx.doi.org/10.1109/isne.2013.6512333.

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Gu, Lei, and Xianling Lu. "Semi-supervised subtractive clustering by seeding." In 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2012. http://dx.doi.org/10.1109/fskd.2012.6234240.

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Zhu, Q., H. F. Zhang, Q. Q. Yang, and Y. B. Yang. "An improved hierarchical clustering algorithm based on subtractive clustering." In International Conference on Computer Science and Technology. WIT Press, 2014. http://dx.doi.org/10.2495/iccst140591.

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Huo, Xing, Jieqing Tan, and Rujing Wang. "Color Transfer Based on Combining Subtractive Clustering with FCM Clustering." In 2007 10th IEEE International Conference on Computer-Aided Design and Computer Graphics. IEEE, 2007. http://dx.doi.org/10.1109/cadcg.2007.4407930.

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Sheng-Wu Xiong, Xiao-Xiao Niu, and Hong-Bing Liu. "Support vector machines based on subtractive clustering." In Proceedings of 2005 International Conference on Machine Learning and Cybernetics. IEEE, 2005. http://dx.doi.org/10.1109/icmlc.2005.1527702.

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Zhi-Hai Sun, Xue-Hui Wei, and Wen-Hui Zhou. "A NystrÖm-based subtractive clustering method." In 2012 International Conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP). IEEE, 2012. http://dx.doi.org/10.1109/icwamtip.2012.6413443.

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Khanna, Kavita, and Navin Rajpal. "Subtractive clustering: A tool for reconstructing noisy curves." In 2014 International Conference on Signal Processing and Integrated Networks (SPIN). IEEE, 2014. http://dx.doi.org/10.1109/spin.2014.6776922.

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Le, Thanh, and Lan Vu. "A novel fuzzy clustering method based on GA, PSO and Subtractive Clustering." In 2020 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2020. http://dx.doi.org/10.1109/csci51800.2020.00063.

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