Academic literature on the topic 'Modified fuzzy c-means (FCM)'
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Journal articles on the topic "Modified fuzzy c-means (FCM)"
Huang, Cheng Quan. "A Modified Fuzzy C-Mean Algorithm for Automatic Clustering Number." Applied Mechanics and Materials 333-335 (July 2013): 1418–21. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.1418.
Full textZhang, Wenyuan, Tianyu Huang, and Jun Chen. "A Robust Bias-Correction Fuzzy Weighted C-Ordered-Means Clustering Algorithm." Mathematical Problems in Engineering 2019 (June 18, 2019): 1–17. http://dx.doi.org/10.1155/2019/5984649.
Full textYang, Qing, Zhi Qiang Wang, and Yan Xu. "Fuzzy C-Means Image Segmentation Algorithm Based on Chaotic Simulated Annealing." Applied Mechanics and Materials 624 (August 2014): 536–39. http://dx.doi.org/10.4028/www.scientific.net/amm.624.536.
Full textDewi Paramitha, Ida Ayu Shinta, Gusti Made Arya Sasmita, and I. Made Sunia Raharja. "Analisis Data Log IDS Snort dengan Algoritma Clustering Fuzzy C-Means." Majalah Ilmiah Teknologi Elektro 19, no. 1 (October 15, 2020): 95. http://dx.doi.org/10.24843/mite.2020.v19i01.p14.
Full textSzilágyi, László, Szidónia Lefkovits, and Sándor M. Szilágyi. "Self-Tuning Possibilistic c-Means Clustering Models." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 27, Supp01 (November 5, 2019): 143–59. http://dx.doi.org/10.1142/s0218488519400075.
Full textLiu, Xiao Li, Yu Ting Guo, Jun Kong, and Jian Zhong Wang. "A Modified Fuzzy C-Means Algorithm Brain MR Images Segmentation with Bias Field Compensation." Advanced Materials Research 756-759 (September 2013): 1349–55. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.1349.
Full textRustam, Koredianto Usman, Mudyawati Kamaruddin, Dina Chamidah, Nopendri, Khaerudin Saleh, Yulinda Eliskar, and Ismail Marzuki. "MODIFIED POSSIBILISTIC FUZZY C-MEANS ALGORITHM FOR CLUSTERING INCOMPLETE DATA SETS." Acta Polytechnica 61, no. 2 (April 30, 2021): 364–77. http://dx.doi.org/10.14311/ap.2021.61.0364.
Full textRayala, Venkat, and Satyanarayan Reddy Kalli. "Big Data Clustering Using Improvised Fuzzy C-Means Clustering." Revue d'Intelligence Artificielle 34, no. 6 (December 31, 2020): 701–8. http://dx.doi.org/10.18280/ria.340604.
Full textWan, Renxia, Yuelin Gao, and Caixia Li. "Weighted Fuzzy-Possibilistic C-Means Over Large Data Sets." International Journal of Data Warehousing and Mining 8, no. 4 (October 2012): 82–107. http://dx.doi.org/10.4018/jdwm.2012100104.
Full textLiu, Qing Feng. "An Extensional Clustering Algorithm of FCM Based on Intuitionistic Extension Index." Advanced Materials Research 490-495 (March 2012): 1372–76. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.1372.
Full textDissertations / Theses on the topic "Modified fuzzy c-means (FCM)"
Hong, Sui. "Experiments with K-Means, Fuzzy c-Means and Approaches to Choose K and C." Honors in the Major Thesis, University of Central Florida, 2006. http://digital.library.ucf.edu/cdm/ref/collection/ETH/id/1224.
Full textBachelors
Engineering and Computer Science
Computer Engineering
Naik, Vaibhav C. "Fuzzy C-means clustering approach to design a warehouse layout." [Tampa, Fla.] : University of South Florida, 2004. http://purl.fcla.edu/fcla/etd/SFE0000437.
Full textMuna, Izza Hasanul. "Modely a metody pro svozové problému v logistice." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2019. http://www.nusl.cz/ntk/nusl-401586.
Full textAtaeian, Seyed Mohsen, and Mehrnaz Jaberi Darbandi. "Analysis of Quality of Experience by applying Fuzzy logic : A study on response time." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-5742.
Full text(14030507), Deepani B. Guruge. "Effective document clustering system for search engines." Thesis, 2008. https://figshare.com/articles/thesis/Effective_document_clustering_system_for_search_engines/21433218.
Full textPeople use web search engines to fill a wide variety of navigational, informational and transactional needs. However, current major search engines on the web retrieve a large number of documents of which only a small fraction are relevant to the user query. The user then has to manually search for relevant documents by traversing a topic hierarchy, into which a collection is categorised. As more information becomes available, it becomes a time consuming task to search for required relevant information.
This research develops an effective tool, the web document clustering (WDC) system, to cluster, and then rank, the output data obtained from queries submitted to a search engine, into three pre-defined fuzzy clusters. Namely closely related, related and not related. Documents in closely related and related documents are ranked based on their context.
The WDC output has been compared against document clustering results from the Google, Vivisimo and Dogpile systems as these where considered the best at the fourth Search Engine Awards [24]. Test data was from standard document sets, such as the TREC-8 [118] data files and the Iris database [38], or 3 from test text retrieval tasks, "Latex", "Genetic Algorithms" and "Evolutionary Algorithms". Our proposed system had as good as, or better results, than that obtained by these other systems. We have shown that the proposed system can effectively and efficiently locate closely related, related and not related, documents among the retrieved document set for queries submitted to a search engine.
We developed a methodology to supply the user with a list of keywords filtered from the initial search result set to further refine the search. Again we tested our clustering results against the Google, Vivisimo and Dogpile systems. In all cases we have found that our WDC performs as well as, or better than these systems.
The contributions of this research are:
- A post-retrieval fuzzy document clustering algorithm that groups documents into closely related, related and not related clusters. This algorithm uses modified fuzzy c-means (FCM) algorithm to cluter documents into predefined intelligent fuzzy clusters and this approach has not been used before.
- The fuzzy WDC system satisfies the user's information need as far as possible by allowing the user to reformulate the initial query. The system prepares an initial word list by selecting a few characteristics terms of high frequency from the first twenty documents in the initial search engine output. The user is then able to use these terms to input a secondary query. The WDC system then creates a second word list, or the context of the user query (COQ), from the closely related documents to provide training data to refine the search. Documents containing words with high frequency from the training list, based on a pre-defined threshold value, are then presented to the user to refine the search by reformulating the query. In this way the context of the user query is built, enabling the user to learn from the keyword list. This approach is not available in current search engine technology.
- A number of modifications were made to the FCM algorithm to improve its performance in web document clustering. A factor swkq is introduced into the membership function as a measure of the amount of overlaping between the components of the feature vector and the cluster prototype. As the FCM algorithm is greatly affected by the values used to initialise the components of cluster prototypes a machine learning approach, using an Evolutionary Algorithm, was used to resolve the initialisation problem.
- Experimental results indicate that the WDC system outperformed Google, Dogpile and the Vivisimo search engines. The post-retrieval fuzzy web document clustering algorithm designed in this research improves the precision of web searches and it also contributes to the knowledge of document retrieval using fuzzy logic.
- A relational data model was used to automatically store data output from the search engine off-line. This takes the processing of data of the Internet off-line, saving resources and making better use of the local CPU.
- This algorithm uses Latent Semantic Indexing (LSI) to rank documents in the closely related and related clusters. Using LSI to rank document is wellknown, however, we are the first to apply it in the context of ranking closely related documents by using COQ to form the term x document matrix in LSI, to obtain better ranking results.
- Adjustments based on document size are proposed for dealing with problems associated with varying document size in the retrieved documents and the effect this has on cluster analysis.
Guo, Jen-Der, and 郭建得. "Heartbeat Case Determination Using the Fuzzy C-Means (FCM) Method on ECG Signals." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/58048189342571486634.
Full text健行科技大學
電子工程系碩士班
103
This paper presents a simple and effective electrocardiogram (ECG) heartbeat species identification method, which includes: (1) ECG signal pre-processor: the aim is to enlarge the body taken from the patient to the ECG signal, and do all kinds of miscellaneous information removal process; (2) ECG signal transmission: the post-processing of the ECG signal to Wi-Fi wireless communication technology is transferred to the receiver; and (3) calculation of the original feature points feature value: according to the received Wi-Fi receiver ECG signal to the middle of the QRS complex, P T wave spread position, a characteristic feature of the original value of each point; selection (4) the main features of the point: the principal component analysis (Principal Component Analysis; PCA) to select the main feature points the aim is to reduce the time the heartbeat species identification; (5) the heartbeat species identification: fuzzy clustering average (Fuzzy C-Means) method to identify the type of cardiac patients heartbeat, the heartbeat of this paper can identify five species occur more frequently, contain normal heartbeat (NORM) and four kinds of abnormal heartbeat. Four kinds of irregular heartbeat were: a left bundle branch block (LBBB), right bundle branch block (RBBB), ventricular premature contractions (VPC), and atrial premature contraction (APC) and so on. Finally, this paper MIT-BIH arrhythmia database related files to assess the effectiveness of the proposed method, the actual testing, identification heartbeat category NORM, LBBB, RBBB, VPC, and APC''s Se were up 98.28%, 90.35 %, 86.97%, 92.19%, and 94.36%. The total average rate of correct judgment TCA was 93.57%.
Qu, Zhong-Zheng, and 瞿忠正. "VQ-based image compression using modified fuzzy C-Means method." Thesis, 1993. http://ndltd.ncl.edu.tw/handle/01706199175015486439.
Full textChoi, Yunsik. "Mapping continuous soil properties using fuzzy C-means (FCM) clustering on soil similarity vectors generated from solim." 2005. http://catalog.hathitrust.org/api/volumes/oclc/62501656.html.
Full textTypescript. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 71-76).
Huang, Chia-tai, and 黃家泰. "Blind Equalization of Inter-Symbol Interference Based on Modified Fuzzy-C-Means Algorithms." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/55257174711396041374.
Full text國立雲林科技大學
電子與資訊工程研究所
94
Abstract – Usually, adaptive equalizer to channel conditions is obtained by inserting a known sequence within the data set: the deconvolution of this sequence should allow the receiver to extract enough knowledge about the channel conditions to tune the filler weights and invert the channel transfer function. Blind equalizers on the contrary, extract their knowledge directly from the channel output without using any training input data. In this thesis, the fuzzy-c-means (FCM) clustering algorithm is used to achieve joint equalization and demodulation of a QAM signal affected by intersymbol interference (ISI) in a blind mode without insertion of a training sequence in the data stream. To improve decision quality, the fuzzy possibilistic c-means (FPCM) algorithm and possibilistic fuzzy c-means algorithm (PFCM) are applied into the blind equalization framework. Furthermore, an accumulative cluster centers concept is proposed to compact the performance degradation of the clustering-based blind equalization when the frame size of data is too small. Computer simulation demonstrates that the proposed algorithms can achieve performance improvement in the system.
yu, Chin, and 游縉. "An Inferior Quality Images Segmentation Algorithm Based on the Modified Fuzzy C-Means Method." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/4y38me.
Full text國立臺北科技大學
自動化科技研究所
106
In this thesis, an images segmentation algorithm based on the modified fuzzy C-means method is proposed to segment the image with low contrast, underexposure or noises accurately. Firstly, based on the histogram equalization, a novel image fusion method is proposed to modify the image with over enhanced and distortion. In addition, we adopt the modified noise filter algorithm to eliminate noises and the details of the image is also preserved. Moreover, in order to segment the image accurately, the proposed modified fuzzy C-means algorithm is utilized to classify the filtered image. Lastly, the experiment results illustrate that the proposed images segmentation algorithm can segment the inferior image effectively.
Book chapters on the topic "Modified fuzzy c-means (FCM)"
Zhang, David, Wangmeng Zuo, and Peng Wang. "Modified Gaussian Models and Fuzzy C-Means." In Computational Pulse Signal Analysis, 231–46. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-4044-3_12.
Full textSingh, Nisha, Vivek Srivastava, and Komal. "Iris Data Classification Using Modified Fuzzy C Means." In Computational Intelligence: Theories, Applications and Future Directions - Volume I, 345–57. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1132-1_27.
Full textYang, Yong, Chongxun Zheng, and Pan Lin. "Image Thresholding via a Modified Fuzzy C-Means Algorithm." In Lecture Notes in Computer Science, 589–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30463-0_74.
Full textLi, Yanling, and Gang Li. "Fuzzy C-Means Cluster Segmentation Algorithm Based on Modified Membership." In Advances in Neural Networks – ISNN 2009, 135–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01510-6_16.
Full textPi, Dechang, Xiaolin Qin, and Peisen Yuan. "A Modified Fuzzy C-Means Algorithm for Association Rules Clustering." In Lecture Notes in Computer Science, 1093–103. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-37275-2_137.
Full textLázaro, Jesús, Jagoba Arias, José L. Martín, and Carlos Cuadrado. "Modified Fuzzy C-Means Clustering Algorithm for Real-Time Applications." In Field Programmable Logic and Application, 1087–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45234-8_126.
Full textNayak, Janmenjoy, Bighnaraj Naik, and H. S. Behera. "Fuzzy C-Means (FCM) Clustering Algorithm: A Decade Review from 2000 to 2014." In Computational Intelligence in Data Mining - Volume 2, 133–49. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-2208-8_14.
Full textBilenia, Aniket, Daksh Sharma, Himanshu Raj, Rahul Raman, and Mahua Bhattacharya. "Brain Tumor Segmentation with Skull Stripping and Modified Fuzzy C-Means." In Information and Communication Technology for Intelligent Systems, 229–37. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1742-2_23.
Full textKobashi, Syoji, Yutaka Hata, Yuri T. Kitamura, Toshiaki Hayakata, and Toshio Yanagida. "Brain State Recognition Using Fuzzy C-Means (FCM) Clustering with Near Infrared Spectroscopy (NIRS)." In Computational Intelligence. Theory and Applications, 124–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45493-4_17.
Full textArumugam, Sajeev Ram, Bharath Bhushan, Monika Arya, Oswalt Manoj, and Syed Muzamil Basha. "Lung Cancer Detection Using Modified Fuzzy C-Means Clustering and Adaptive Neuro-Fuzzy Network." In Lecture Notes in Electrical Engineering, 733–42. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4831-2_60.
Full textConference papers on the topic "Modified fuzzy c-means (FCM)"
Suprihatin, Iwan Tri Riyadi Yanto, Nursyiva Irsalinda, Tuti Purwaningsih, Haviluddin, and Aji Prasetya Wibawa. "A performance of modified fuzzy C-means (FCM) and chicken swarm optimization (CSO)." In 2017 3rd International Conference on Science in Information Technology (ICSITech). IEEE, 2017. http://dx.doi.org/10.1109/icsitech.2017.8257105.
Full textLiu, L. F., Z. D. Sun, X. Y. Zhou, J. F. Han, B. Jing, Y. Y. Pan, H. T. Zhao, and Y. Neng. "A New Algorithm of Modified Fuzzy C Means Clustering (FCM) and the Prediction of Carbonate Fluid." In 76th EAGE Conference and Exhibition 2014. Netherlands: EAGE Publications BV, 2014. http://dx.doi.org/10.3997/2214-4609.20140801.
Full textNasution, Bahrul Ilmi, and Robert Kurniawan. "Robustness of classical fuzzy C-means (FCM)." In 2018 International Conference on Information and Communications Technology (ICOIACT). IEEE, 2018. http://dx.doi.org/10.1109/icoiact.2018.8350729.
Full textSaad, Mohamed Fadhel, and Adel M. Alimi. "Improved Modified Suppressed Fuzzy C-Means." In 2010 2nd International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE, 2010. http://dx.doi.org/10.1109/ipta.2010.5586754.
Full textPei, Jihong, Xuan Yang, Xinbo Gao, and Weixing Xie. "Weighting exponent m in fuzzy C-means (FCM) clustering algorithm." In Multispectral Image Processing and Pattern Recognition, edited by Jun Shen, Sharatchandra Pankanti, and Runsheng Wang. SPIE, 2001. http://dx.doi.org/10.1117/12.441637.
Full textZhang, Yu, Huan Wu, Jianzhong Zhang, Jingjing Wang, and Xueqiang Zou. "TW-FCM: An Improved Fuzzy-C-Means Algorithm for SPIT Detection." In 2018 27th International Conference on Computer Communication and Networks (ICCCN). IEEE, 2018. http://dx.doi.org/10.1109/icccn.2018.8487369.
Full textSantos, Gabriel Marcondes, Emmanuel Tavares Ferreira Affonso, Alisson Marques Silva, and Gray Farias Moita. "Fuzzy C-Means com Método Wrapper Com Baixo Custo Computacional de Seleção de Atributos." In Congresso Brasileiro de Inteligência Computacional. SBIC, 2021. http://dx.doi.org/10.21528/cbic2021-87.
Full textQu, Fuheng, Yating Hu, Yaohong Xue, and Yong Yang. "A modified possibilistic fuzzy c-means clustering algorithm." In 2013 9th International Conference on Natural Computation (ICNC). IEEE, 2013. http://dx.doi.org/10.1109/icnc.2013.6818096.
Full textBordogna, Gloria, and Gabriella Pasi. "Hierarchical-Hyperspherical Divisive Fuzzy C-Means (H2D-FCM) Clustering for Information Retrieval." In 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology. IEEE, 2009. http://dx.doi.org/10.1109/wi-iat.2009.104.
Full textKang, Jiayin, and Wenjuan Zhang. "Fingerprint Image Segmentation Using Modified Fuzzy C-Means Algorithm." In 2009 3rd International Conference on Bioinformatics and Biomedical Engineering (iCBBE). IEEE, 2009. http://dx.doi.org/10.1109/icbbe.2009.5162858.
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