Relevant bibliographies by topics / Fuzzy c-means clustering analysis

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  2. Dissertations / Theses
  3. Books
  4. Book chapters
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Academic literature on the topic 'Fuzzy c-means clustering analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 February 2022

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Journal articles on the topic "Fuzzy c-means clustering analysis"

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Rayala, 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.

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Clustering emerged as powerful mechanism to analyze the massive data generated by modern applications; the main aim of it is to categorize the data into clusters where objects are grouped into the particular category. However, there are various challenges while clustering the big data recently. Deep Learning has been powerful paradigm for big data analysis, this requires huge number of samples for training the model, which is time consuming and expensive. This can be avoided though fuzzy approach. In this research work, we design and develop an Improvised Fuzzy C-Means (IFCM)which comprises the encoder decoder Convolutional Neural Network (CNN) model and Fuzzy C-means (FCM) technique to enhance the clustering mechanism. Encoder decoder based CNN is used for learning feature and faster computation. In general, FCM, we introduce a function which measure the distance between the cluster center and instance which helps in achieving the better clustering and later we introduce Optimized Encoder Decoder (OED) CNN model for improvising the performance and for faster computation. Further in order to evaluate the proposed mechanism, three distinctive data types namely Modified National Institute of Standards and Technology (MNIST), fashion MNIST and United States Postal Service (USPS) are used, also evaluation is carried out by considering the performance metric like Accuracy, Adjusted Rand Index (ARI) and Normalized Mutual Information (NMI). Moreover, comparative analysis is carried out on each dataset and comparative analysis shows that IFCM outperforms the existing model.
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Hidayat, Syahroni, Ria Rismayati, Muhammad Tajuddin, and Ni Luh Putu Merawati. "Segmentation of university customers loyalty based on RFM analysis using fuzzy c-means clustering." Jurnal Teknologi dan Sistem Komputer 8, no. 2 (March 11, 2020): 133–39. http://dx.doi.org/10.14710/jtsiskom.8.2.2020.133-139.

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One of the strategic plans of the developing universities in obtaining new students is forming a partnership with surrounding high schools. However, partnerships made does not always behave as expected. This paper presented the segmentation technique to the previous new student admission dataset using the integration of recency, frequency, and monetary (RFM) analysis and fuzzy c-means (FCM) algorithm to evaluate the loyalty of the entire school that has bound the partnership with the institution. The dataset is converted using the RFM approach before processed with the FCM algorithm. The result reveals that the schools can be segmented, respectively, as high potential (SP), potential (P), low potential (CP), and very low potential (KP) categories with PCI value 0.86. From the analysis of SP, P, and CP, only 71 % of 52 school partners categorized as loyal partners.
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A, Dharmarajan, and Velmurugan T. "Performance Analysis on K-Means and Fuzzy C-Means Clustering Algorithms Using CT-DICOM Images of Lung Cancer." Journal of Advanced Research in Dynamical and Control Systems 11, no. 0009-SPECIAL ISSUE (September 25, 2019): 494–502. http://dx.doi.org/10.5373/jardcs/v11/20192597.

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Khang, Tran Dinh, Nguyen Duc Vuong, Manh-Kien Tran, and Michael Fowler. "Fuzzy C-Means Clustering Algorithm with Multiple Fuzzification Coefficients." Algorithms 13, no. 7 (June 30, 2020): 158. http://dx.doi.org/10.3390/a13070158.

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Clustering is an unsupervised machine learning technique with many practical applications that has gathered extensive research interest. Aside from deterministic or probabilistic techniques, fuzzy C-means clustering (FCM) is also a common clustering technique. Since the advent of the FCM method, many improvements have been made to increase clustering efficiency. These improvements focus on adjusting the membership representation of elements in the clusters, or on fuzzifying and defuzzifying techniques, as well as the distance function between elements. This study proposes a novel fuzzy clustering algorithm using multiple different fuzzification coefficients depending on the characteristics of each data sample. The proposed fuzzy clustering method has similar calculation steps to FCM with some modifications. The formulas are derived to ensure convergence. The main contribution of this approach is the utilization of multiple fuzzification coefficients as opposed to only one coefficient in the original FCM algorithm. The new algorithm is then evaluated with experiments on several common datasets and the results show that the proposed algorithm is more efficient compared to the original FCM as well as other clustering methods.
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Kunwar, Vineeta, A. Sai Sabitha, Tanupriya Choudhury, and Archit Aggarwal. "Chronic Kidney Disease Using Fuzzy C-Means Clustering Analysis." International Journal of Business Analytics 6, no. 3 (July 2019): 43–64. http://dx.doi.org/10.4018/ijban.2019070104.

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Medical industries are encountered with challenges like providing quality services to patients, correct diagnosis and effective treatments at reasonable cost. Data mining has become a necessity and provides solutions to many important and critical health related concerns. It is the process to mine knowledgeable information from voluminous medical data sets. It plays an essential role in improving medical decision making and helps to investigate trends in patient conditions which can be used by doctors for disease diagnosis. Clustering is an unsupervised learning technique that groups object with high similarity together. Chronic kidney disease (CKD) causes renal failure and kidney dysfunction. It has become an important health issue with the number of cases on the rise every year. This article presents analysis and detection of Chronic Kidney Disease using Fuzzy C Means (FCM) clustering which is effective in mining complex data having fuzzy relationships among members. FCM will investigate and group together the patients having CKD and Not CKD. The simulation and coding are done in MATLAB.
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Rosadi, R., Akamal, R. Sudrajat, B. Kharismawan, and Y. A. Hambali. "Student academic performance analysis using fuzzy C-means clustering." IOP Conference Series: Materials Science and Engineering 166 (January 2017): 012036. http://dx.doi.org/10.1088/1757-899x/166/1/012036.

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Hu, Qiongqiong, Yiyang Li, Yong Ge, Yu-an Zhang, Qinglian Ma, and Makoto Sakamoto. "Clustering Analysis Based on Improved Fuzzy C - Means Algorithm." Proceedings of International Conference on Artificial Life and Robotics 23 (February 2, 2018): 276–81. http://dx.doi.org/10.5954/icarob.2018.os1-5.

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Wan, Shuting, and Xiong Zhang. "Bearing fault diagnosis based on teager energy entropy and mean-shift fuzzy C-means." Structural Health Monitoring 19, no. 6 (April 14, 2020): 1976–88. http://dx.doi.org/10.1177/1475921720910710.

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Feature extraction and fault recognition of vibration signals are two important parts of bearing fault diagnosis. In this article, a fault diagnosis method based on teager energy entropy of each wavelet subband and improved fuzzy C-means is proposed. First, bearing vibration signal is decomposed into wavelet packet and normalized teager energy entropy feature matrix is constructed as clustering index. Principal component analysis is applied to the high-dimensional teager energy entropy feature matrix, and the principal components are determined by cumulative contribution rate to construct feature vectors. Then, the mean-shift method is used to search for the high probability density region of principal components so as to determine the cluster number and cluster center. Finally, fuzzy C-means is used to update the clustering center and membership value, and confirm the optimal clustering center and the type of clustering. Through simulated and experimental analysis, the proposed method has two advantages. The feature vector constructed by this method has better specificity than wavelet energy entropy. The initial clustering center of fuzzy C-means is confirmed by the mean-shift method, which can improve the clustering performance of fuzzy C-means and solve the misclassification without preknowing the number of categories.
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Sahu, Sanat Kumar, and A. K. Shrivas. "Analysis and Comparison of Clustering Techniques for Chronic Kidney Disease With Genetic Algorithm." International Journal of Computer Vision and Image Processing 8, no. 4 (October 2018): 16–25. http://dx.doi.org/10.4018/ijcvip.2018100102.

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The purpose of this article is to weigh up the foremost imperative features of Chronic Kidney Disease (CKD). This study is based mostly on three cluster techniques like; K means, Fuzzy c-means and hierarchical clustering. The authors used evolutionary techniques like genetic algorithms (GA) to extend the performance of the clustering model. The performance of these three clusters: live parameter purity, entropy, and Adjusted Rand Index (ARI) have been contemplated. The best purity is obtained by the K-means clustering technique, 96.50%; whereas, Fuzzy C-means clustering received 93.50% and hierarchical clustering was the lowest at 92. 25%. After using evolutionary technique Genetic Algorithm as Feature selection technique, the best purity is obtained by hierarchical clustering, 97.50%, compared to K –means clustering, 96.75%, and Fuzzy C-means clustering at 94.00%.
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Komori, Osamu, and Shinto Eguchi. "A Unified Formulation of k-Means, Fuzzy c-Means and Gaussian Mixture Model by the Kolmogorov–Nagumo Average." Entropy 23, no. 5 (April 24, 2021): 518. http://dx.doi.org/10.3390/e23050518.

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Clustering is a major unsupervised learning algorithm and is widely applied in data mining and statistical data analyses. Typical examples include k-means, fuzzy c-means, and Gaussian mixture models, which are categorized into hard, soft, and model-based clusterings, respectively. We propose a new clustering, called Pareto clustering, based on the Kolmogorov–Nagumo average, which is defined by a survival function of the Pareto distribution. The proposed algorithm incorporates all the aforementioned clusterings plus maximum-entropy clustering. We introduce a probabilistic framework for the proposed method, in which the underlying distribution to give consistency is discussed. We build the minorize-maximization algorithm to estimate the parameters in Pareto clustering. We compare the performance with existing methods in simulation studies and in benchmark dataset analyses to demonstrate its highly practical utilities.
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Dissertations / Theses on the topic "Fuzzy c-means clustering analysis"

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Kanade, Parag M. "Fuzzy ants as a clustering concept." [Tampa, Fla.] : University of South Florida, 2004. http://purl.fcla.edu/fcla/etd/SFE0000397.

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Camara, Assa. "Využití fuzzy množin ve shlukové analýze se zaměřením na metodu Fuzzy C-means Clustering." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2020. http://www.nusl.cz/ntk/nusl-417051.

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This master thesis deals with cluster analysis, more specifically with clustering methods that use fuzzy sets. Basic clustering algorithms and necessary multivariate transformations are described in the first chapter. In the practical part, which is in the third chapter we apply fuzzy c-means clustering and k-means clustering on real data. Data used for clustering are the inputs of chemical transport model CMAQ. Model CMAQ is used to approximate concentration of air pollutants in the atmosphere. To the data we will apply two different clustering methods. We have used two different methods to select optimal weighting exponent to find data structure in our data. We have compared all 3 created data structures. The structures resembled each other but with fuzzy c-means clustering, one of the clusters did not resemble any of the clustering inputs. The end of the third chapter is dedicated to an attempt to find a regression model that finds the relationship between inputs and outputs of model CMAQ.
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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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Rodgers, Sarah. "Application of the fuzzy c-means clustering algorithm to the analysis of chemical structures." Thesis, University of Sheffield, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.412772.

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FANEGAN, JULIUS BOLUDE. "A FUZZY MODEL FOR ESTIMATING REMAINING LIFETIME OF A DIESEL ENGINE." University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1188951646.

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Zubková, Kateřina. "Text mining se zaměřením na shlukovací a fuzzy shlukovací metody." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2018. http://www.nusl.cz/ntk/nusl-382412.

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This thesis is focused on cluster analysis in the field of text mining and its application to real data. The aim of the thesis is to find suitable categories (clusters) in the transcribed calls recorded in the contact center of Česká pojišťovna a.s. by transferring these textual documents into the vector space using basic text mining methods and the implemented clustering algorithms. From the formal point of view, the thesis contains a description of preprocessing and representation of textual data, a description of several common clustering methods, cluster validation, and the application itself.
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Pondini, Alessio. "Tenacizzazione di laminati compositi mediante l'utilizzo di nanofibre in PVDF." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amslaurea.unibo.it/8463/.

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Analisi riguardante la tenacizzazione della matrice di laminati compositi. Lo scopo è quello di aumentare la resistenza alla frattura di modo I e, a tal proposito, sono stati modificati gli interstrati di alcuni provini tramite l’introduzione di strati, di diverso spessore, di nanofibre in polivinilidenfluoruro (PVDF). La valutazione di tale metodo di rinforzo è stata eseguita servendosi di dati ottenuti tramite prove sperimentali svolte in laboratorio direttamente dal sottoscritto, che si è occupato dell’elaborazione dei dati servendosi di tecniche e algoritmi di recente ideazione. La necessità primaria per cui si cerca di rinforzare la matrice risiede nel problema più sentito dei laminati compositi in opera da molto tempo: la delaminazione. Oltre a verificare le proprietà meccaniche dei provini modificati sottoponendoli a test DCB, si è utilizzata una tecnica basata sulle emissioni acustiche per comprendere più approfonditamente l’inizio della delaminazione e i meccanismi di rottura che si verificano durante le prove. Quest’ultimi sono illustrati servendosi di un algoritmo di clustering, detto Fuzzy C-means, tramite il quale è stato possibile identificare ogni segnale come appartenente o meno ad un determinato modo di rottura. I risultati mostrano che il PVDF, applicato nelle modalità esposte, è in grado di aumentare la resistenza alla frattura di modo I divenendo contemporaneamente causa di un diverso modo di propagazione della frattura. Infine l’elaborato presenta alcune micrografie delle superfici di rottura, le quali appoggiano i risultati ottenuti nelle precedenti fasi di analisi.
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Ataeian, 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.

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To be successful in today's competitive market, service providers should look at user's satisfaction as a critical key. In order to gain a better understanding of customers' expectations, a proper evaluations which considers intrinsic characteristics of perceived quality of service is needed. Due to the subjective nature of quality, the vagueness of human judgment and the uncertainty about the degree of users' linguistic satisfaction, fuzziness is associated with quality of experience. Considering the capability of Fuzzy logic in dealing with imprecision and qualitative knowledge, it would be wise to apply it as a powerful mathematical tool for analyzing the quality of experience (QoE). This thesis proposes a fuzzy procedure to evaluate the quality of experience. In our proposed methodology, we provide a fuzzy relationship between QoE and Quality of Service (QoS) parameters. To identify this fuzzy relationship a new term called Fuzzi ed Opinion Score (FOS) representing a fuzzy quality scale is introduced. A fuzzy data mining method is applied to construct the required number of fuzzy sets. Then, the appropriate membership functions describing fuzzy sets are modeled and compared with each other. The proposed methodology will assist service providers for better decision-making and resource management.
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Zettervall, Hang. "Fuzzy Set Theory Applied to Make Medical Prognoses for Cancer Patients." Doctoral thesis, Blekinge Tekniska Högskola [bth.se], Faculty of Engineering - Department of Mathematics and Natural Sciences, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-00574.

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As we all know the classical set theory has a deep-rooted influence in the traditional mathematics. According to the two-valued logic, an element can belong to a set or cannot. In the former case, the element’s membership degree will be assigned to one, whereas in the latter case it takes the zero value. With other words, a feeling of imprecision or fuzziness in the two-valued logic does not exist. With the rapid development of science and technology, more and more scientists have gradually come to realize the vital importance of the multi-valued logic. Thus, in 1965, Professor Lotfi A. Zadeh from Berkeley University put forward the concept of a fuzzy set. In less than 60 years, people became more and more familiar with fuzzy set theory. The theory of fuzzy sets has been turned to be a favor applied to many fields. The study aims to apply some classical and extensional methods of fuzzy set theory in life expectancy and treatment prognoses for cancer patients. The research is based on real-life problems encountered in clinical works by physicians. From the introductory items of the fuzzy set theory to the medical applications, a collection of detailed analysis of fuzzy set theory and its extensions are presented in the thesis. Concretely speaking, the Mamdani fuzzy control systems and the Sugeno controller have been applied to predict the survival length of gastric cancer patients. In order to keep the gastric cancer patients, already examined, away from the unnecessary suffering from surgical operation, the fuzzy c-means clustering analysis has been adopted to investigate the possibilities for operation contra to nonoperation. Furthermore, the approach of point set approximation has been adopted to estimate the operation possibilities against to nonoperation for an arbitrary gastric cancer patient. In addition, in the domain of multi-expert decision-making, the probabilistic model, the model of 2-tuple linguistic representations and the hesitant fuzzy linguistic term sets (HFLTS) have been utilized to select the most consensual treatment scheme(s) for two separate prostate cancer patients. The obtained results have supplied the physicians with reliable and helpful information. Therefore, the research work can be seen as the mathematical complements to the physicians’ queries.
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Moura, Ronildo Pinheiro de Ara?jo. "Algoritmos de agrupamentos fuzzy intervalares e ?ndice de valida??o para agrupamento de dados simb?licos do tipo intervalo." Universidade Federal do Rio Grande do Norte, 2014. http://repositorio.ufrn.br:8080/jspui/handle/123456789/18111.

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Made available in DSpace on 2014-12-17T15:48:11Z (GMT). No. of bitstreams: 1 RonildoPAM_DISSERT.pdf: 2783175 bytes, checksum: c268ade677ca4b8c543ccc014b0aafef (MD5) Previous issue date: 2014-02-21
Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior
Symbolic Data Analysis (SDA) main aims to provide tools for reducing large databases to extract knowledge and provide techniques to describe the unit of such data in complex units, as such, interval or histogram. The objective of this work is to extend classical clustering methods for symbolic interval data based on interval-based distance. The main advantage of using an interval-based distance for interval-based data lies on the fact that it preserves the underlying imprecision on intervals which is usually lost when real-valued distances are applied. This work includes an approach allow existing indices to be adapted to interval context. The proposed methods with interval-based distances are compared with distances punctual existing literature through experiments with simulated data and real data interval
A An?lise de Dados Simb?licos (SDA) tem como objetivo prover mecanismos de redu??o de grandes bases de dados para extra??o do conhecimento e desenvolver m?todos que descrevem esses dados em unidades complexas, tais como, intervalos ou um histograma. O objetivo deste trabalho ? estender m?todos de agrupamento cl?ssicos para dados simb?licos intervalares baseados em dist?ncias essencialmente intervalares. A principal vantagem da utiliza??o de uma dist?ncia essencialmente intervalar est? no fato da preserva??o da imprecis?o inerente aos intervalos, pois a imprecis?o ? normalmente perdida quando as dist?ncias valoradas em R s?o aplicadas. Este trabalho inclui uma abordagem que permite adaptar ?ndices de valida??o de agrupamento existentes para o contexto intervalar. Os m?todos propostos com dist?ncias essencialmente intervalares s?o comparados a dist?ncias pontuais existentes na literatura atrav?s de experimentos realizados com dados sint?ticos e reais intervalares
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Books on the topic "Fuzzy c-means clustering analysis"

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

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Fatemi-Ghomi, N. Texture segmentation using wavelet packets and c-means fuzzy clustering. Guildford: Dept. of Electronic and Electrical Engineering, 1995.

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Fitriyati, Nina. Penentuan karakteristik alumni UIN Syarif Hidayatullah Jakarta periode wisuda April 2008 dan Juli 2008 menggunakan metode fuzzy C-means clustering. Jakarta: Kerjasama Lembaga Penelitian UIN Jakarta dengan UIN Jakarta Press, 2008.

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Miyamoto, Sadaaki, Hidetomo Ichihashi, and Katsuhiro Honda. Algorithms for Fuzzy Clustering: Methods in c-Means Clustering with Applications. Springer, 2010.

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Book chapters on the topic "Fuzzy c-means clustering analysis"

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Guo, Zhe, and Furong Wang. "Telecommunications User Behaviors Analysis Based on Fuzzy C-Means Clustering." In Future Generation Information Technology, 585–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17569-5_57.

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Yang, Yan, Qing-you Liu, and Ying He. "Similarity Analysis of Oilfield Development Indices by Fuzzy C-Means Clustering." In Advances in Intelligent and Soft Computing, 399–407. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28592-9_41.

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Wang, Jikui, Quanfu Shi, Zhengguo Yang, and Feiping Nie. "Clustering by Unified Principal Component Analysis and Fuzzy C-Means with Sparsity Constraint." In Algorithms and Architectures for Parallel Processing, 337–51. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60239-0_23.

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Wallace, Jeffrey, Mozaffari N. Homayoun, Li Pan, and Nitish V. Thakor. "Fuzzy C-means Clustering Analysis to Monitor Tissue Perfusion with Near Infrared Imaging." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2001, 1213–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45468-3_167.

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Turhan, Meltem. "Genetic Fuzzy Clustering by means of discovering membership functions." In Advances in Intelligent Data Analysis Reasoning about Data, 383–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0052856.

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Winkler, Roland, Frank Klawonn, and Rudolf Kruse. "Problems of Fuzzy c-Means Clustering and Similar Algorithms with High Dimensional Data Sets." In Challenges at the Interface of Data Analysis, Computer Science, and Optimization, 79–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-24466-7_9.

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Alanzado, Arnold C., and Sadaaki Miyamoto. "Fuzzy c-Means Clustering in the Presence of Noise Cluster for Time Series Analysis." In Modeling Decisions for Artificial Intelligence, 156–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11526018_16.

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Zhong, Zhi, Qingdong Song, and Bin Ni. "Application of Fuzzy C-Means Clustering Based on Principal Component Analysis in Computer Forensics." In Lecture Notes in Electrical Engineering, 7–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27314-8_2.

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Pang, Liang, Kai Xiao, Alei Liang, and Haibing Guan. "A Improved Clustering Analysis Method Based on Fuzzy C-Means Algorithm by Adding PSO Algorithm." In Lecture Notes in Computer Science, 231–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28942-2_21.

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Mishra, Purnendu, and Nilamani Bhoi. "Kalman Filtering Based Fuzzy C-Means Clustering and Artificial Neural Network for Classification of Microarray Data." In Learning and Analytics in Intelligent Systems, 305–15. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30271-9_28.

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Conference papers on the topic "Fuzzy c-means clustering analysis"

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Fernandes, Marta P., Joaquim L. Viegas, Susana M. Vieira, and Joao M. Sousa. "Analysis of residential natural gas consumers using fuzzy c-means clustering." In 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2016. http://dx.doi.org/10.1109/fuzz-ieee.2016.7737865.

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Daiqiang Peng, Yun Ling, and Yang Wang. "Improving fuzzy c-means clustering based on local membership variation." In 2010 International Conference on Image Analysis and Signal Processing. IEEE, 2010. http://dx.doi.org/10.1109/iasp.2010.5476098.

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Mondal, Tanmoy, Mickael Coustaty, Petra Gomez-Kramer, and Jean-Marc Ogier. "Learning Free Document Image Binarization Based on Fast Fuzzy C-Means Clustering." In 2019 International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2019. http://dx.doi.org/10.1109/icdar.2019.00223.

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Li, Jie, Chao-hsien Chu, and Yunfeng Wang. "An In-depth Analysis of Fuzzy C-Means Clustering for Cellular Manufacturing." In 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2008. http://dx.doi.org/10.1109/fskd.2008.433.

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Xu, Guangmei, Jiwen Dong, Jin Zhou, Yingxu Wang, Bozhan Dang, Dong Wang, Lin Wang, and Shiyuan Han. "An improved fuzzy c-means clustering algorithm with guided filter for Image Segmentation." In 2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC). IEEE, 2018. http://dx.doi.org/10.1109/spac46244.2018.8965448.

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Xu, Suqin, Jie Chen, and Guoxing Gao. "Remote sensing ocean data analyses using fuzzy C-Means clustering." In Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, edited by Henri Maître, Hong Sun, Bangjun Lei, and Jufu Feng. SPIE, 2009. http://dx.doi.org/10.1117/12.833199.

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Dureja, Ajay. "Comparative Analysis of Curve Reconstruction using Fuzzy C Means and Subtractive Clustering." In 2018 International Conference on Recent Innovations in Electrical, Electronics & Communication Engineering (ICRIEECE). IEEE, 2018. http://dx.doi.org/10.1109/icrieece44171.2018.9009149.

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Wang, Jie, Hongshi Huang, Xiaoli Li, and Yingfang Ao. "Application of the fuzzy C-means clustering algorithm in plantar pressure analysis." In 2016 Chinese Control and Decision Conference (CCDC). IEEE, 2016. http://dx.doi.org/10.1109/ccdc.2016.7531329.

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Lin, Kuo-Ping, Ching-Lin Lin, Kuo-Chen Hung, Yu-Ming Lu, and Ping-Feng Pai. "Developing kernel intuitionistic fuzzy c-means clustering for e-learning customer analysis." In 2012 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE, 2012. http://dx.doi.org/10.1109/ieem.2012.6838017.

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Li, Qingshan. "Mobile User Network Behavior Analysis Based on Improved Fuzzy C-Means Clustering." In 2016 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS). IEEE, 2016. http://dx.doi.org/10.1109/icitbs.2016.81.

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Reports on the topic "Fuzzy c-means clustering analysis"

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Kersten, P. R. Fuzzy Robust Statistics for Application to the Fuzzy c-Means Clustering Algorithm. Fort Belvoir, VA: Defense Technical Information Center, December 1993. http://dx.doi.org/10.21236/ada274719.

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