Relevant bibliographies by topics / Fuzzy clusters

Academic literature on the topic 'Fuzzy clusters'

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Published: 10 December 2022
Last updated: 19 February 2023

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Journal articles on the topic "Fuzzy clusters"

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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.

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Two well-known drawbacks in fuzzy clustering are the requirement of assigning in advance the number of clusters and random initialization of cluster centers. The quality of the final fuzzy clusters depends heavily on the initial choice of the number of clusters and the initialization of the clusters, then, it is necessary to apply a validity index to measure the compactness and the separability of the final clusters and run the clustering algorithm several times. We propose a new fuzzy C-means algorithm in which a validity index based on the concepts of maximum fuzzy energy and minimum fuzzy entropy is applied to initialize the cluster centers and to find the optimal number of clusters and initial cluster centers in order to obtain a good clustering quality, without increasing time consumption. We test our algorithm on UCI (University of California at Irvine) machine learning classification datasets comparing the results with the ones obtained by using well-known validity indices and variations of fuzzy C-means by using optimization algorithms in the initialization phase. The comparison results show that our algorithm represents an optimal trade-off between the quality of clustering and the time consumption.
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Ryoo, 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.

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Fuzzy clustering has been broadly applied to classify data into K clusters by assigning membership probabilities of each data point close to K centroids. Such a function has been applied into characterizing the clusters associated with a statistical model such as structural equation modeling. The characteristics identified by the statistical model further define the clusters as heterogeneous groups selected from a population. Recently, such statistical model has been formulated as fuzzy clusterwise generalized structured component analysis (fuzzy clusterwise GSCA). The same as in fuzzy clustering, the clusters are enumerated to infer the population and its parameters within the fuzzy clusterwise GSCA. However, the identification of clusters in fuzzy clustering is a difficult task because of the data-dependence of classification indexes, which is known as a cluster validity problem. We examined the cluster validity problem within the fuzzy clusterwise GSCA framework and proposed a new criterion for selecting the most optimal number of clusters using both fit indexes of the GSCA and the fuzzy validity indexes in fuzzy clustering. The criterion, named the FIT-FHV method combining a fit index, FIT, from GSCA and a cluster validation measure, FHV, from fuzzy clustering, performed better than any other indices used in fuzzy clusterwise GSCA.
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GREKOUSIS, 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.

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Choosing the optimal number of clusters is a key issue in cluster analysis. Especially when dealing with more spatial clustering, things tend to be more complicated. Cluster validation helps to determine the appropriate number of clusters present in a dataset. Furthermore, cluster validation evaluates and assesses the results of clustering algorithms. There are numerous methods and techniques for choosing the optimal number of clusters via crisp and fuzzy clustering. In this paper, we introduce a new index for fuzzy clustering to determine the optimal number of clusters. This index is not another metric for calculating compactness or separation among partitions. Instead, the index uses several existing indices to give a degree, or fuzziness, to the optimal number of clusters. In this way, not only do the objects in a fuzzy cluster get a membership value, but the number of clusters to be partitioned is given a value as well. The new index is used in the fuzzy c-means algorithm for the geodemographic segmentation of 285 postal codes.
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Azad, 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.

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Clustering is one of the important methods for prolonging the network lifetime in wireless sensor networks (WSNs). It involves grouping of sensor nodes into clusters and electing cluster heads (CHs) for all the clusters. CHs collect the data from respective cluster’s nodes and forward the aggregated data to base station. A major challenge in WSNs is to select appropriate cluster heads. In this paper, we present a fuzzy decision-making approach for the selection of cluster heads. Fuzzy multiple attribute decision-making (MADM) approach is used to select CHs using three criteria including residual energy, number of neighbors, and the distance from the base station of the nodes. The simulation results demonstrate that this approach is more effective in prolonging the network lifetime than the distributed hierarchical agglomerative clustering (DHAC) protocol in homogeneous environments.
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BORGELT, 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.

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Resampling methods are among the best approaches to determine the number of clusters in prototype-based clustering. The core idea is that with the right choice for the number of clusters basically the same cluster structures should be obtained from subsamples of the given data set, while a wrong choice should produce considerably varying cluster structures. In this paper I give an overview how such resampling approaches can be transferred to fuzzy and probabilistic clustering. I study several cluster comparison measures, which can be parameterized with t-norms, and report experiments that provide some guidance which of them may be the best choice.
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Borisov, 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.

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A feature of energy systems (ESs) is the diversity of objects, as well as the variety and manifold of the interconnections between them. A method for monitoring ESs clusters is proposed based on the combined use of a fuzzy cognitive approach and dynamic clustering. A fuzzy cognitive approach allows one to represent the interdependencies between ESs objects in the form of fuzzy impact relations, the analysis results of which are used to substantiate indicators for fuzzy clustering of ESs objects and to analyze the stability of clusters and ESs. Dynamic clustering methods are used to monitor the cluster structure of ESs, namely, to assess the drift of cluster centers, to determine the disappearance or emergence of new clusters, and to unite or separate clusters of ESs.
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Rajkumar, 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.

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A hard partition clustering algorithm assigns equally distant points to one of the clusters, where each datum has the probability to appear in simultaneous assignment to further clusters. The fuzzy cluster analysis assigns membership coefficients of data points which are equidistant between two clusters so the information directs have a place toward in excess of one cluster in the meantime. For a subset of CiteScore dataset, fuzzy clustering (fanny) and fuzzy c-means (fcm) algorithms were implemented to study the data points that lie equally distant from each other. Before analysis, clusterability of the dataset was evaluated with Hopkins statistic which resulted in 0.4371, a value &lt; 0.5, indicating that the data is highly clusterable. The optimal clusters were determined using NbClust package, where it is evidenced that 9 various indices proposed 3 cluster solutions as best clusters. Further, appropriate value of fuzziness parameter <em>m</em> was evaluated to determine the distribution of membership values with variation in <em>m</em> from 1 to 2. Coefficient of variation (CV), also known as relative variability was evaluated to study the spread of data. The time complexity of fuzzy clustering (fanny) and fuzzy c-means algorithms were evaluated by keeping data points constant and varying number of clusters.
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Chen, 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.

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Abstract Fuzzy clustering is a popular unsupervised learning method that is used in cluster analysis. Fuzzy clustering allows a data point to belong to two or more clusters. Fuzzy c-means is the most well-known method that is applied to cluster analysis, however, the shortcoming is that the number of clusters need to be predefined. This paper proposes a clustering approach based on Particle Swarm Optimization (PSO). This PSO approach determines the optimal number of clusters automatically with the help of a threshold vector. The algorithm first randomly partitions the data set within a preset number of clusters, and then uses a reconstruction criterion to evaluate the performance of the clustering results. The experiments conducted demonstrate that the proposed algorithm automatically finds the optimal number of clusters. Furthermore, to visualize the results principal component analysis projection, conventional Sammon mapping, and fuzzy Sammon mapping were used
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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 (April 29, 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, the Fuzzy C-Means Clustering algorithm has a higher level of validity than the Fuzzy Subtractive Clustering algorithm so it can be said that the Fuzzy C-Means algorithm performs the cluster process better than with the Fuzzy Subtractive method in clustering alumni data. The number of clusters that have the best fitness value / the most optimal number of clusters based on the CE and MPC validity index is 5 clusters. The cluster that has the best characteristics is the 1st cluster which has 514 members (36.82% of the total alumni). With the characteristics of having an average GPA of 3.3617, the average study period is 7.8102 semesters and an average TA work period of 4.9596 months.
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Honda, 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.

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Fuzzyc-means (FCM) is the fuzzy version ofc-means clustering, in which memberships are fuzzified by introducing an additional parameter into the linear objective function of the weighted sum of distances between datapoints and cluster centers. Regularization of hardc-means clustering is another approach to fuzzification, in which regularization terms such as entropy and quadratic terms have been adopted. We generalized the fuzzification concept and propose a new approach to fuzzy clustering in which linear weights of hardc-means clustering are replaced by nonlinear ones through regularization. Numerical experiments demonstrated that the proposed algorithm has the characteristic features of the standard FCM algorithm and of regularization approaches. One of the proposed nonlinear weights makes it possible to both to attract data to clusters and to repulse different clusters. This feature derives different types of fuzzy classification functions in both probabilistic and possibilistic models.
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Dissertations / Theses on the topic "Fuzzy clusters"

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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.

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Made available in DSpace on 2014-12-17T15:47:00Z (GMT). No. of bitstreams: 1 RogerioRV_TESE.pdf: 769325 bytes, checksum: ddaac964e1c74fba3533b5cdd90927b2 (MD5) Previous issue date: 2012-03-30
Coordena??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
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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.

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Felizardo, 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.

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Dissertação para obtenção do Grau de Mestre em Engenharia Informática
Relational 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.
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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.

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Franco, 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.

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Trabalho apresentado no âmbito do Mestrado em Engenharia Informática, como requisito parcial para obtenção do grau de Mestre em Engenharia Informática
O 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.
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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.

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In this paper we present a fuzzy voting scheme for cluster algorithms. This fuzzy voting method allows us to combine several runs of cluster algorithms resulting in a common fuzzy partition. This helps us to overcome instabilities of the cluster algorithms and results in a better clustering.
Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"
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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.

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Timm, 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.

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Pangaonkar, 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.

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Stetco, 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.

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Cluster analysis, the automatic procedure by which large data sets can be split into similar groups of objects (clusters), has innumerable applications in a wide range of problem domains. Improvements in clustering quality (as captured by internal validation indexes) and speed (number of iterations until cost function convergence), the main focus of this work, have many desirable consequences. They can result, for example, in faster and more precise detection of illness onset based on symptoms or it could provide investors with a rapid detection and visualization of patterns in financial time series and so on. Partitional clustering, one of the most popular ways of doing cluster analysis, can be classified into two main categories: hard (where the clusters discovered are disjoint) and soft (also known as fuzzy; clusters are non-disjoint, or overlapping). In this work we consider how improvements in the speed and solution quality of the soft partitional clustering algorithm Fuzzy C-means (FCM) can be achieved through more careful and informed initialization based on data content. By carefully selecting the cluster centers in a way which disperses the initial cluster centers through the data space, the resulting FCM++ approach samples starting cluster centers during the initialization phase. The cluster centers are well spread in the input space, resulting in both faster convergence times and higher quality solutions. Moreover, we allow the user to specify a parameter indicating how far and apart the cluster centers should be picked in the dataspace right at the beginning of the clustering procedure. We show FCM++'s superior behaviour in both convergence times and quality compared with existing methods, on a wide rangeof artificially generated and real data sets. We consider a case study where we propose a methodology based on FCM++for pattern discovery on synthetic and real world time series data. We discuss a method to utilize both Pearson correlation and Multi-Dimensional Scaling in order to reduce data dimensionality, remove noise and make the dataset easier to interpret and analyse. We show that by using FCM++ we can make an positive impact on the quality (with the Xie Beni index being lower in nine out of ten cases for FCM++) and speed (with on average 6.3 iterations compared with 22.6 iterations) when trying to cluster these lower dimensional, noise reduced, representations of the time series. This methodology provides a clearer picture of the cluster analysis results and helps in detecting similarly behaving time series which could otherwise come from any domain. Further, we investigate the use of Spherical Fuzzy C-Means (SFCM) with the seeding mechanism used for FCM++ on news text data retrieved from a popular British newspaper. The methodology allows us to visualize and group hundreds of news articles based on the topics discussed within. The positive impact made by SFCM++ translates into a faster process (with on average 12.2 iterations compared with the 16.8 needed by the standard SFCM) and a higher quality solution (with the Xie Beni being lower for SFCM++ in seven out of every ten runs).
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Books on the topic "Fuzzy clusters"

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Miyamoto, Sadaakio. Fuzzy sets in information retrieval and cluster analysis. Dordrecht: Kluwer Academic Publishers, 1990.

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Deimer, Reinhard. Unscharfe Clusteranalysemethoden: Eine problemorientierte Darstellung zur unscharfen Klassifikation gemischter Daten. Idstein: Schulz-Kirchner, 1986.

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Frank, Höppner, ed. Fuzzy cluster analysis: Methods for classification, data analysis, and image recognition. Chichester ; New York: J. Wiley, 1999.

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Miyamoto, Sadaaki. Algorithms for fuzzy clustering: Methods in c-means clustering with applications. Berlin: Springer, 2008.

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Miyamoto, Sadaaki. Fuzzy sets in information retrieval and cluster analysis. Dordrecht: Kluwer Academic, 1990.

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Miyamoto, Sadaaki. Fuzzy Sets in Information Retrieval and Cluster Analysis. Dordrecht: Springer Netherlands, 1990.

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Miyamoto, Sadaaki. Fuzzy Sets in Information Retrieval and Cluster Analysis. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-015-7887-5.

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Boreiko, Dimitri. EMU and accession countries: Fuzzy cluster analysis of membership. Wien: Oesterreichische Nationalbank, 2002.

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1939-, Bezdek James C., ed. Fuzzy models and algorithms for pattern recognition and image processing. Boston: Kluwer Academic Publ., 1999.

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Viattchenin, Dmitri A. A heuristic approach to possibilistic clustering: Algorithms and applications. Heidelberg: Springer, 2013.

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Book chapters on the topic "Fuzzy clusters"

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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.

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Klawonn, 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.

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Bodjanova, 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.

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Hai, 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.

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Plocinski, 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.

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Viswanathan, 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.

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Liu, 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.

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Hotta, 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.

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Choi, 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.

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Nascimento, 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.

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Conference papers on the topic "Fuzzy clusters"

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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.

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Torra, 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.

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Erilli, 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.

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Economic indicators in economic policies have an important place in determining the levels of development. Determining and classifying the existing social and economic structures of countries is very important for examining the development states and possible development tendencies of countries and forming regional development policies in line with these. The aim in cluster analysis, is to classify datas in to similarity and perform useful knowledge for the researcher. Cluster analysis, which became more popular among the subjects of statistical classification in recent years, can give more reliable results when there is apriori knowledge about number of clusters. Fuzzy models interested in fuzzy model structures and try to estimate system behaviours that has no knowledge about their structure. Fuzzy Cluster Analysis is try to decompose the groups which membership degrees cannot be determined. When the number of datas and variables increased or cluster structures came to closer for all, Cluster analysis has given more successful results then the other cluster analysis methods. In this study, Turkish Republics were classified in terms of the indicators determined by using Fuzzy C-Means (FCM) and Gath Geva methods which are frequently used in fuzzy clustering analysis. The objective was to find out the common class structures of Turkish Republics which came out with the disintegration of the Soviet Union in 1991 and which experienced economic similar problems and thus to help countries in the same clusters in similar economic planning. Results are also compared between fuzzy and crisp clustering analysis methods.
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Bharill, 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.

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Sharko, 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.

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Suleman, 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.

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This paper examines online job advertisements to identify the type of skills and other attributes required for higher education graduates in European countries. The data were collected from European job websites in 2019 (n=1,752) for any country and occupation having a job offer requiring higher education. The empirical analysis starts with a fuzzy clustering to identify typical skill patterns required by employers. Six clusters emerge from the data; five can be labelled as adaptability skills, foreign languages, specific skills, work attributes, and managing skills. The remaining one is referred to as null cluster with no distinctive required skill. Subsequently, we examine the occupation and employment conditions associated with each fuzzy cluster. Despite the demand for graduates, the service and sales related occupations prevail in the null cluster. In other five well-defined clusters we find a mix of skills of some high-qualified occupations, and search for specific skills acquired through work experience.The findings raise the question about the assignment of graduates in less qualified occupations.
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Cocana-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.

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Borgelt, 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.

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Cocana-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.

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Honda, 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.

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Reports on the topic "Fuzzy clusters"

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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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Cluster analysis of the efficiency of the recreational forest use of the region by separate components of the recreational forest use potential is provided in the article. The main stages of the cluster analysis of the recreational forest use level based on the predetermined components were determined. Among the agglomerative methods of cluster analysis, intended for grouping and combining the objects of study, it is common to distinguish the three most common types: the hierarchical method or the method of tree clustering; the K-means Clustering Method and the two-step aggregation method. For the correct selection of clusters, a comparative analysis of several methods was performed: arithmetic mean ranks, hierarchical methods followed by dendrogram construction, K- means method, which refers to reference methods, in which the number of groups is specified by the user. The cluster analysis of forestries by twenty analytical grounds was not proved by analysis of variance, so the re-clustering of certain objects was carried out according to the nine most significant analytical features. As a result, the forestry was clustered into four clusters. The conducted cluster analysis with the use of different methods allows us to state that their combination helps to select reasonable groupings, clearly illustrate the clustering procedure and rank the obtained forestry clusters.
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