Academic literature on the topic 'Fuzzy clusters'
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Journal articles on the topic "Fuzzy clusters"
Martino, Ferdinando Di, and Salvatore Sessa. "A New Validity Index Based on Fuzzy Energy and Fuzzy Entropy Measures in Fuzzy Clustering Problems." Entropy 22, no. 11 (October 23, 2020): 1200. http://dx.doi.org/10.3390/e22111200.
Full textRyoo, Ji Hoon, Seohee Park, Seongeun Kim, and Hyun Suk Ryoo. "Efficiency of Cluster Validity Indexes in Fuzzy Clusterwise Generalized Structured Component Analysis." Symmetry 12, no. 9 (September 14, 2020): 1514. http://dx.doi.org/10.3390/sym12091514.
Full textGREKOUSIS, GEORGE. "GIVING FUZZINESS TO SPATIAL CLUSTERS: A NEW INDEX FOR CHOOSING THE OPTIMAL NUMBER OF CLUSTERS." International Journal on Artificial Intelligence Tools 22, no. 03 (June 2013): 1350009. http://dx.doi.org/10.1142/s0218213013500097.
Full textAzad, Puneet, and Vidushi Sharma. "Cluster Head Selection in Wireless Sensor Networks under Fuzzy Environment." ISRN Sensor Networks 2013 (February 24, 2013): 1–8. http://dx.doi.org/10.1155/2013/909086.
Full textBORGELT, CHRISTIAN. "RESAMPLING FOR FUZZY CLUSTERING." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 15, no. 05 (October 2007): 595–614. http://dx.doi.org/10.1142/s0218488507004893.
Full textBorisov, Vadim, Maksim Dli, Artem Vasiliev, Yaroslav Fedulov, Elena Kirillova, and Nikolay Kulyasov. "Energy System Monitoring Based on Fuzzy Cognitive Modeling and Dynamic Clustering." Energies 14, no. 18 (September 15, 2021): 5848. http://dx.doi.org/10.3390/en14185848.
Full textRajkumar, K. Varada, Adimulam Yesubabu, and K. Subrahmanyam. "Fuzzy clustering and fuzzy c-means partition cluster analysis and validation studies on a subset of citescore dataset." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 4 (August 1, 2019): 2760. http://dx.doi.org/10.11591/ijece.v9i4.pp2760-2770.
Full textChen, Min, and Simone A. Ludwig. "Particle Swarm Optimization Based Fuzzy Clustering Approach to Identify Optimal Number of Clusters." Journal of Artificial Intelligence and Soft Computing Research 4, no. 1 (January 1, 2014): 43–56. http://dx.doi.org/10.2478/jaiscr-2014-0024.
Full textSupartha, 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 (April 29, 2021): 41. http://dx.doi.org/10.19184/isj.v6i1.22077.
Full textHonda, Katsuhiro, and Hidetomo Ichihashi. "A Regularization Approach to Fuzzy Clustering with Nonlinear Membership Weights." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 1 (January 20, 2007): 28–34. http://dx.doi.org/10.20965/jaciii.2007.p0028.
Full textDissertations / Theses on the topic "Fuzzy clusters"
Vargas, Rogerio Rodrigues de. "Uma nova forma de calcular os centros dos Clusters em algoritmos de agrupamento tipo fuzzy c-means." Universidade Federal do Rio Grande do Norte, 2012. http://repositorio.ufrn.br:8080/jspui/handle/123456789/17949.
Full textCoordena??o de Aperfei?oamento de Pessoal de N?vel Superior
Clustering data is a very important task in data mining, image processing and pattern recognition problems. One of the most popular clustering algorithms is the Fuzzy C-Means (FCM). This thesis proposes to implement a new way of calculating the cluster centers in the procedure of FCM algorithm which are called ckMeans, and in some variants of FCM, in particular, here we apply it for those variants that use other distances. The goal of this change is to reduce the number of iterations and processing time of these algorithms without affecting the quality of the partition, or even to improve the number of correct classifications in some cases. Also, we developed an algorithm based on ckMeans to manipulate interval data considering interval membership degrees. This algorithm allows the representation of data without converting interval data into punctual ones, as it happens to other extensions of FCM that deal with interval data. In order to validate the proposed methodologies it was made a comparison between a clustering for ckMeans, K-Means and FCM algorithms (since the algorithm proposed in this paper to calculate the centers is similar to the K-Means) considering three different distances. We used several known databases. In this case, the results of Interval ckMeans were compared with the results of other clustering algorithms when applied to an interval database with minimum and maximum temperature of the month for a given year, referring to 37 cities distributed across continents
Agrupar dados ? uma tarefa muito importante em minera??o de dados, processamento de imagens e em problemas de reconhecimento de padr?es. Um dos algoritmos de agrupamentos mais popular ? o Fuzzy C-Means (FCM). Esta tese prop?e aplicar uma nova forma de calcular os centros dos clusters no algoritmo FCM, que denominamos de ckMeans, e que pode ser tamb?m aplicada em algumas variantes do FCM, em particular aqui aplicamos naquelas variantes que usam outras dist?ncias. Com essa modifica??o, pretende-se reduzir o n?mero de itera??es e o tempo de processamento desses algoritmos sem afetar a qualidade da parti??o ou at? melhorar o n?mero de classifica??es corretas em alguns casos. Tamb?m, desenvolveu-se um algoritmo baseado no ckMeans para manipular dados intervalares considerando graus de pertin?ncia intervalares. Este algoritmo possibilita a representa??o dos dados sem convers?o dos dados intervalares para pontuais, como ocorre com outras extens?es do FCM que lidam com dados intervalares. Para validar com as metodologias propostas, comparou-se o agrupamento ckMeans com os algoritmos K-Means (pois o algoritmo proposto neste trabalho para c?lculo dos centros se assemelha ? do K-Means) e FCM, considerando tr?s dist?ncias diferentes. Foram utilizadas v?rias bases de dados conhecidas. No caso, os resultados do ckMeans intervalar, foram comparadas com outros algoritmos de agrupamento intervalar quando aplicadas a uma base de dados intervalar com a temperatura m?nima e m?xima do m?s de um determinado ano, referente a 37 cidades distribu?das entre os continentes
Frigui, Hichem. "New approaches for robust clustering and for estimating the optimal number of clusters /." free to MU campus, to others for purchase, 1997. http://wwwlib.umi.com/cr/mo/fullcit?p9842528.
Full textFelizardo, Rui Miguel Meireles. "A study on parallel versus sequential relational fuzzy clustering methods." Master's thesis, Faculdade de Ciências e Tecnologia, 2011. http://hdl.handle.net/10362/5663.
Full textRelational Fuzzy Clustering is a recent growing area of study. New algorithms have been developed,as FastMap Fuzzy c-Means (FMFCM) and the Fuzzy Additive Spectral Clustering Method(FADDIS), for which it had been obtained interesting experimental results in the corresponding founding works. Since these algorithms are new in the context of the Fuzzy Relational clustering community, not many experimental studies are available. This thesis comes in response to the need of further investigation on these algorithms, concerning a comparative experimental study from the two families of algorithms: the parallel and the sequential versions. These two families of algorithms differ in the way they cluster data. Parallel versions extract clusters simultaneously from data and need the number of clusters as an input parameter of the algorithms, while the sequential versions extract clusters one-by-one until a stop condition is verified, being the number of clusters a natural output of the algorithm. The algorithms are studied in their effectiveness on retrieving good cluster structures by analysing the quality of the partitions as well as the determination of the number of clusters by applying several validation measures. An extensive simulation study has been conducted over two data generators specifically constructed for the algorithms under study, in particular to study their robustness for data with noise. Results with benchmark real data are also discussed. Particular attention is made on the most adequate pre-processing on relational data, in particular on the pseudo-inverse Laplacian transformation.
Garcia, Ian. "Eliminating Redundant and Less-informative RSS News Articles Based on Word Similarity and A Fuzzy Equivalence Relation." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd1688.pdf.
Full textFranco, Pedro Guerra de Almeida. "Fuzzy clustering não supervisionado na detecção automática de regiões de upwelling a partir de mapas de temperatura da superfície oceânica." Master's thesis, FCT - UNL, 2009. http://hdl.handle.net/10362/2383.
Full textO afloramento costeiro (upwelling) ao largo da costa de Portugal Continental é um fenómeno bem estudado na literatura oceanográfica. No entanto, existem poucos trabalhos na literatura científica sobre a sua detecção automática, em particular utilizando técnicas de clustering. Algoritmos de agrupamento difuso (fuzzy clustering) têm sido bastante explorados na área de detecção remota e segmentação de imagem, e investigação recente mostrou que essas técnicas conseguem resultados promissores na detecção do upwelling a partir de mapas de temperatura da superfície do oceano, obtidos por imagens de satélite. No trabalho a desenvolver nesta dissertação, propõe-se definir um método que consiga identificar automaticamente a região que define o fenómeno. Como objecto de estudo, foram analisados dois conjuntos independentes de mapas de temperatura, num total de 61 mapas, cobrindo a diversidade de cenários em que o upwelling ocorre. Focando o domínio do problema, foi desenvolvido trabalho de pesquisa bibliográfica ao nível de literatura de referência e estudos mais recentes, principalmente sobre os temas de técnicas de agrupamento, agrupamento difuso e a sua aplicação à segmentação de imagem. Com base num dos algoritmos com mais influência na literatura, o Fuzzy c-means (FCM), foi desenvolvida uma nova abordagem, utilizando o método de inicialização ‘Anomalous Pattern’, que tenta resolver dois problemas base do FCM: a validação do melhor número de clusters e a dependência da inicialização aleatória. Após um estudo das condições de paragem do novo algoritmo, AP-FCM, estabeleceu-se uma parametrização que determina automaticamente um bom número de clusters. Análise aos resultados obtidos mostra que as segmentações geradas são de qualidade elevada, reproduzindo fidedignamente as estruturas presentes nos mapas originais, e que, computacionalmente, o AP-FCM é mais eficiente que o FCM. Foi ainda implementado um outro algoritmo, com base numa técnica de Histogram Thresholding, que, obtendo também boas segmentações, não permite uma parametrização para a definição automática do número de grupos. A partir das segmentações obtidas, foi desenvolvido um módulo de definição de features, a partir das quais se criou um critério composto que permite a identificação automática do cluster que delimita a região de upwelling.
Dimitriadou, Evgenia, Andreas Weingessel, and Kurt Hornik. "Fuzzy voting in clustering." SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, 1999. http://epub.wu.ac.at/742/1/document.pdf.
Full textSeries: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"
Hammah, Reginald Edmund. "Intelligent delineation of rock discontinuity data using fuzzy cluster analysis." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape11/PQDD_0012/NQ41436.pdf.
Full textTimm, Heiko. "Fuzzy-Clusteranalyse Methoden zur Exploration von Daten mit fehlenden Werten sowie klassifizierten Daten /." [S.l. : s.n.], 2002. http://deposit.ddb.de/cgi-bin/dokserv?idn=965011097.
Full textPangaonkar, Manali. "Exploratory Study of Fuzzy Clustering and Set-Distance Based Validation Indexes." University of Cincinnati / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1353342433.
Full textStetco, Adrian. "An investigation into fuzzy clustering quality and speed : fuzzy C-means with effective seeding." Thesis, University of Manchester, 2017. https://www.research.manchester.ac.uk/portal/en/theses/an-investigation-into-fuzzy-clustering-quality-and-speed-fuzzy-cmeans-with-effective-seeding(fac3eab2-919a-436c-ae9b-1109b11c1cc2).html.
Full textBooks on the topic "Fuzzy clusters"
Miyamoto, Sadaakio. Fuzzy sets in information retrieval and cluster analysis. Dordrecht: Kluwer Academic Publishers, 1990.
Find full textDeimer, Reinhard. Unscharfe Clusteranalysemethoden: Eine problemorientierte Darstellung zur unscharfen Klassifikation gemischter Daten. Idstein: Schulz-Kirchner, 1986.
Find full textFrank, Höppner, ed. Fuzzy cluster analysis: Methods for classification, data analysis, and image recognition. Chichester ; New York: J. Wiley, 1999.
Find full textMiyamoto, Sadaaki. Algorithms for fuzzy clustering: Methods in c-means clustering with applications. Berlin: Springer, 2008.
Find full textMiyamoto, Sadaaki. Fuzzy sets in information retrieval and cluster analysis. Dordrecht: Kluwer Academic, 1990.
Find full textMiyamoto, Sadaaki. Fuzzy Sets in Information Retrieval and Cluster Analysis. Dordrecht: Springer Netherlands, 1990.
Find full textMiyamoto, Sadaaki. Fuzzy Sets in Information Retrieval and Cluster Analysis. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-015-7887-5.
Full textBoreiko, Dimitri. EMU and accession countries: Fuzzy cluster analysis of membership. Wien: Oesterreichische Nationalbank, 2002.
Find full text1939-, Bezdek James C., ed. Fuzzy models and algorithms for pattern recognition and image processing. Boston: Kluwer Academic Publ., 1999.
Find full textViattchenin, Dmitri A. A heuristic approach to possibilistic clustering: Algorithms and applications. Heidelberg: Springer, 2013.
Find full textBook chapters on the topic "Fuzzy clusters"
Bing, Zhou, Jun-yi Shen, and Qin-ke Peng. "HYBRID: From Atom-Clusters to Molecule-Clusters." In Fuzzy Systems and Knowledge Discovery, 1151–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11539506_144.
Full textKlawonn, Frank, and Georg Hoffmann. "Using Fuzzy Cluster Analysis to Find Interesting Clusters." In Building Bridges between Soft and Statistical Methodologies for Data Science, 231–39. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15509-3_31.
Full textBodjanova, Slavka. "Partial Defuzzification of Fuzzy Clusters." In Classification, Clustering, and Data Analysis, 27–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-56181-8_2.
Full textHai, Phan Nhat, Dino Ienco, Pascal Poncelet, and Maguelonne Teisseire. "Mining Fuzzy Moving Object Clusters." In Advanced Data Mining and Applications, 100–114. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35527-1_9.
Full textPlocinski, Jerzy(George). "Constructing Fuzzy Clusters for Marketing Research." In Proceedings of the 1995 World Marketing Congress, 369–75. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17311-5_52.
Full textViswanathan, M., Y. K. Yang, and T. K. Whangbo. "Distributed Data Mining on Clusters with Bayesian Mixture Modeling." In Fuzzy Systems and Knowledge Discovery, 1207–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11539506_151.
Full textLiu, Jian. "Finding and Evaluating Fuzzy Clusters in Networks." In Lecture Notes in Computer Science, 17–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13498-2_3.
Full textHotta, Seiji, Kohei Inoue, and Kiichi Urahama. "Extraction of Fuzzy Clusters from Weighted Graphs." In Knowledge Discovery and Data Mining. Current Issues and New Applications, 442–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45571-x_51.
Full textChoi, Kyungmee, Deok-Hwan Kim, and Taeryon Choi. "Estimating the Number of Clusters Using Multivariate Location Test Statistics." In Fuzzy Systems and Knowledge Discovery, 373–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11881599_43.
Full textNascimento, Susana, Rui Felizardo, and Boris Mirkin. "Thematic Fuzzy Clusters with an Additive Spectral Approach." In Progress in Artificial Intelligence, 446–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24769-9_33.
Full textConference papers on the topic "Fuzzy clusters"
Anderson, Derek T., James M. Keller, Ozy Sjahputera, James C. Bezdek, and Mihail Popescu. "Comparing soft clusters and partitions." In 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2011. http://dx.doi.org/10.1109/fuzzy.2011.6007474.
Full textTorra, Vicenc, Laya Aliahmadipour, and Anders Dahlbom. "Fuzzy, I-fuzzy, and H-fuzzy partitions to describe clusters." In 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2016. http://dx.doi.org/10.1109/fuzz-ieee.2016.7737731.
Full textErilli, Necati Alp, and Çağatay Karaköy. "Classification of Turkish Republics with Specific Economic Indicators in Fuzzy Clustering Analysis." In International Conference on Eurasian Economies. Eurasian Economists Association, 2015. http://dx.doi.org/10.36880/c06.01253.
Full textBharill, Neha, and Aruna Tiwari. "Enhanced cluster validity index for the evaluation of optimal number of clusters for Fuzzy C-Means algorithm." In 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2014. http://dx.doi.org/10.1109/fuzz-ieee.2014.6891591.
Full textSharko, John, and Georges Grinstein. "Visualizing Fuzzy Clusters Using RadViz." In 2009 13th International Conference Information Visualisation, IV. IEEE, 2009. http://dx.doi.org/10.1109/iv.2009.74.
Full textSuleman, Abdul, Fátima Suleman, and Filipa Cunha. "Employability skills of graduates:Insights from job advertisements." In Sixth International Conference on Higher Education Advances. Valencia: Universitat Politècnica de València, 2020. http://dx.doi.org/10.4995/head20.2020.11029.
Full textCocana-Fernandez, Alberto, Luciano Sanchez, and Jose Ranilla. "A software tool to efficiently manage the energy consumption of HPC clusters." In 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2015. http://dx.doi.org/10.1109/fuzz-ieee.2015.7338079.
Full textBorgelt, C., and R. Kruse. "Finding the Number of Fuzzy Clusters by Resampling." In 2006 IEEE International Conference on Fuzzy Systems. IEEE, 2006. http://dx.doi.org/10.1109/fuzzy.2006.1681693.
Full textCocana-Fernandez, Alberto, Emilio San Jose Guiote, Jose Ranilla, and Luciano Sanchez. "Improving EECluster to optimize the carbon footprint and operating costs of HPC clusters." In 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2019. http://dx.doi.org/10.1109/fuzz-ieee.2019.8859001.
Full textHonda, Katsuhiro, Akira Notsu, and Hidetomo Ichihashi. "Collaborative filtering by sequential extraction of user-item clusters based on structural balancing approach." In 2009 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2009. http://dx.doi.org/10.1109/fuzzy.2009.5277251.
Full textReports on the topic "Fuzzy clusters"
Kryzhanivs'kyi, Evstakhii, Liliana Horal, Iryna Perevozova, Vira Shyiko, Nataliia Mykytiuk, and Maria Berlous. Fuzzy cluster analysis of indicators for assessing the potential of recreational forest use. [б. в.], October 2020. http://dx.doi.org/10.31812/123456789/4470.
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