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Journal articles on the topic 'Fuzzyc- means clustering'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Miyamoto, Sadaaki. "Formulation of Fuzzyc-Means Clustering Using Calculus of Variations and Twofold Membership Clusters." Journal of Advanced Computational Intelligence and Intelligent Informatics 12, no. 5 (2008): 454–60. http://dx.doi.org/10.20965/jaciii.2008.p0454.

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A membership matrix of fuzzyc-means clustering is associated with corresponding fuzzy classification rules as membership functions defined on the whole data space. We directly derive such functions in fuzzyc-means and possibilistic clustering using the calculus of variations, generalizing ordinary fuzzyc-means and deriving new twofold membership clustering using this framework.

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Hamasuna, Yukihiro, Yasunori Endo, and Sadaaki Miyamoto. "On Tolerant Fuzzyc-Means Clustering." Journal of Advanced Computational Intelligence and Intelligent Informatics 13, no. 4 (2009): 421–28. http://dx.doi.org/10.20965/jaciii.2009.p0421.

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This paper presents a new type of clustering algorithms by using a tolerance vector called tolerant fuzzyc-means clustering (TFCM). In the proposed algorithms, the new concept of tolerance vector plays very important role. In the original concept of tolerance, a tolerance vector attributes to each data. This concept is developed to handle data flexibly, that is, a tolerance vector attributes not only to each data but also each cluster. Using the new concept, we can consider the influence of clusters to each data by the tolerance. First, the new concept of tolerance is introduced into optimizat

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Hwang, Jeongsik, and Sadaaki Miyamoto. "Kernel Functions Derived from Fuzzy Clustering and Their Application to Kernel Fuzzyc-Means." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 1 (2011): 90–94. http://dx.doi.org/10.20965/jaciii.2011.p0090.

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Among widely used kernel functions, such as support vector machines, in data analysis, the Gaussian kernel is most often used. This kernel arises in entropy-based fuzzyc-means clustering. There is reason, however, to check whether other types of functions used in fuzzyc-means are also kernels. Using completely monotone functions, we show they can be kernels if a regularization constant proposed by Ichihashi is introduced. We also show how these kernel functions are applied to kernel-based fuzzyc-means clustering, which outperform the Gaussian kernel in a typical example.

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Yamamoto, Takeshi, Katsuhiro Honda, Akira Notsu, and Hidetomo Ichihashi. "A Comparative Study on TIBA Imputation Methods in FCMdd-Based Linear Clustering with Relational Data." Advances in Fuzzy Systems 2011 (2011): 1–10. http://dx.doi.org/10.1155/2011/265170.

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Relational fuzzy clustering has been developed for extracting intrinsic cluster structures of relational data and was extended to a linear fuzzy clustering model based on Fuzzyc-Medoids (FCMdd) concept, in which Fuzzyc-Means-(FCM-) like iterative algorithm was performed by defining linear cluster prototypes using two representative medoids for each line prototype. In this paper, the FCMdd-type linear clustering model is further modified in order to handle incomplete data including missing values, and the applicability of several imputation methods is compared. In several numerical experiments,

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Kinoshita, Naohiko, and Yasunori Endo. "On Objective-Based Rough Hard and Fuzzyc-Means Clustering." Journal of Advanced Computational Intelligence and Intelligent Informatics 19, no. 1 (2015): 29–35. http://dx.doi.org/10.20965/jaciii.2015.p0029.

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Clustering is one of the most popular unsupervised classification methods. In this paper, we focus on rough clustering methods based on rough-set representation. Rough k-Means (RKM) is one of the rough clustering method proposed by Lingras et al. Outputs of many clustering algorithms, including RKM depend strongly on initial values, so we must evaluate the validity of outputs. In the case of objectivebased clustering algorithms, the objective function is handled as the measure. It is difficult, however to evaluate the output in RKM, which is not objective-based. To solve this problem, we propo

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Kanzawa, Yuchi. "A Maximizing Model of Spherical Bezdek-Type Fuzzy Multi-Medoids Clustering." Journal of Advanced Computational Intelligence and Intelligent Informatics 19, no. 6 (2015): 738–46. http://dx.doi.org/10.20965/jaciii.2015.p0738.

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This paper proposes three modifications for the maximizing model of spherical Bezdek-type fuzzyc-means clustering (msbFCM). First, we use multi-medoids instead of centroids (msbFMMdd), which is similar to modifying fuzzyc-means to fuzzy multi-medoids. Second, we kernelize msbFMMdd (K-msbFMMdd). msbFMMdd can only be applied to objects in the first quadrant of the unit hypersphere, whereas its kernelized form can be applied to a wider class of objects. The third modification is a spectral clustering approach to K-msbFMMdd using a certain assumption. This approach improves the local convergence p

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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 (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.

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Zhang, Jian, and Ling Shen. "An Improved Fuzzyc-Means Clustering Algorithm Based on Shadowed Sets and PSO." Computational Intelligence and Neuroscience 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/368628.

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To organize the wide variety of data sets automatically and acquire accurate classification, this paper presents a modified fuzzyc-means algorithm (SP-FCM) based on particle swarm optimization (PSO) and shadowed sets to perform feature clustering. SP-FCM introduces the global search property of PSO to deal with the problem of premature convergence of conventional fuzzy clustering, utilizes vagueness balance property of shadowed sets to handle overlapping among clusters, and models uncertainty in class boundaries. This new method uses Xie-Beni index as cluster validity and automatically finds t

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Raj, A. Stanley, D. Hudson Oliver, and Y. Srinivas. "Geoelectrical Data Inversion by Clustering Techniques of Fuzzy Logic to Estimate the Subsurface Layer Model." International Journal of Geophysics 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/134834.

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Soft computing based geoelectrical data inversion differs from conventional computing in fixing the uncertainty problems. It is tractable, robust, efficient, and inexpensive. In this paper, fuzzy logic clustering methods are used in the inversion of geoelectrical resistivity data. In order to characterize the subsurface features of the earth one should rely on the true field oriented data validation. This paper supports the field data obtained from the published results and also plays a crucial role in making an interdisciplinary approach to solve complex problems. Three clustering algorithms

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Murata, Ryuichi, Yasunori Endo, Hideyuki Haruyama, and Sadaaki Miyamoto. "On Fuzzy c-Means for Data with Tolerance." Journal of Advanced Computational Intelligence and Intelligent Informatics 10, no. 5 (2006): 673–81. http://dx.doi.org/10.20965/jaciii.2006.p0673.

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This paper presents two new clustering algorithms which are based on the entropy regularized fuzzyc-means and can treat data with some errors. First, the tolerance is formulated and introduce into optimization problems of clustering. Next, the problems are solved using Kuhn-Tucker conditions. Last, the algorithms are constructed based on the results of solving the problems.

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11

Endo, Yasunori, Yasushi Hasegawa, Yukihiro Hamasuna, and Sadaaki Miyamoto. "Fuzzyc-Means for Data with Rectangular Maximum Tolerance Range." Journal of Advanced Computational Intelligence and Intelligent Informatics 12, no. 5 (2008): 461–66. http://dx.doi.org/10.20965/jaciii.2008.p0461.

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This paper provides new clustering algorithms for data with tolerance. Tolerance is understood in a broad sense, e.g., calculation errors and loss of attribute of data. The concept of tolerance is modified by using new concept of tolerance vector. First, the concept is explained and optimization problems of clustering are formulated using the vectors. Second, the problems are solved using Karush-Kuhn-Tucker conditions. Third, the new clustering algorithms are constructed by using the solutions of the problems. Moreover, the effectiveness of proposed algorithms is verified through some numerica

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Yasuda, Makoto, and Yasuyuki Orito. "Multi-qExtension of Tsallis Entropy Based Fuzzyc-Means Clustering." Journal of Advanced Computational Intelligence and Intelligent Informatics 18, no. 3 (2014): 289–96. http://dx.doi.org/10.20965/jaciii.2014.p0289.

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Tsallis entropy is aq-parameter extension of Shannon entropy. Based on the Tsallis entropy, we have introduced an entropy maximization method to fuzzyc-means clustering (FCM), and developed a new clustering algorithm using a single-qvalue. In this article, we propose a multi-qextension of the conventional single-qmethod. In this method, theqs are assigned individually to each cluster. Eachqvalue is determined so that the membership function fits the corresponding cluster distribution. This is done to improve the accuracy of clustering over that of the conventional single-qmethod. Experiments a

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Ubukata, Seiki, Keisuke Umado, Akira Notsu, and Katsuhiro Honda. "Characteristics of Rough SetC-Means Clustering." Journal of Advanced Computational Intelligence and Intelligent Informatics 22, no. 4 (2018): 551–64. http://dx.doi.org/10.20965/jaciii.2018.p0551.

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HardC-means (HCM), which is one of the most popular clustering techniques, has been extended by using soft computing approaches such as fuzzy theory and rough set theory. FuzzyC-means (FCM) and roughC-means (RCM) are respectively fuzzy and rough set extensions of HCM. RCM can detect the positive and the possible regions of clusters by using the lower and the upper areas which are respectively analogous to the lower and the upper approximations in rough set theory. RCM-type methods have the problem that the original definitions of the lower and the upper approximations are not actually used. In

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Honda, Katsuhiro, Shunnya Oshio, and Akira Notsu. "Fuzzy Co-Clustering Induced by Multinomial Mixture Models." Journal of Advanced Computational Intelligence and Intelligent Informatics 19, no. 6 (2015): 717–26. http://dx.doi.org/10.20965/jaciii.2015.p0717.

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A close connection between fuzzyc-means (FCM) and Gaussian mixture models (GMMs) have been discussed and several extended FCM algorithms were induced by the GMMs concept, where fuzzy partitions are proved to be more useful for revealing intrinsic cluster structures than probabilistic ones. Co-clustering is a promising technique for summarizing cooccurrence information such as document-keyword frequencies. In this paper, a fuzzy co-clustering model is induced based on the multinomial mixture models (MMMs) concept, in which the degree of fuzziness of both object and item fuzzy memberships can be

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Kanzawa, Yuchi. "Fuzzy Co-Clustering Algorithms Based on Fuzzy Relational Clustering and TIBA Imputation." Journal of Advanced Computational Intelligence and Intelligent Informatics 18, no. 2 (2014): 182–89. http://dx.doi.org/10.20965/jaciii.2014.p0182.

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In this paper, two types of fuzzy co-clustering algorithms are proposed. First, it is shown that the base of the objective function for the conventional fuzzy co-clustering method is very similar to the base for entropy-regularized fuzzy nonmetric model. Next, it is shown that the non-sense clustering problem in the conventional fuzzy co-clustering algorithms is identical to that in fuzzy nonmetric model algorithms, in the case that all dissimilarities among rows and columns are zero. Based on this discussion, a method is proposed applying entropy-regularized fuzzy nonmetric model after all di

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Fu, Sheng, Kun Liu, Yonggang Xu, and Yi Liu. "Rolling Bearing Diagnosing Method Based on Time Domain Analysis and Adaptive FuzzyC-Means Clustering." Shock and Vibration 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/9412787.

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Vibration signal analysis is one of the most effective methods for mechanical fault diagnosis. Available part of the information is always concealed in component noise, which makes it much more difficult to detect the defection, especially at early stage of the development. This paper presents a new approach for mechanical fault diagnosis based on time domain analysis and adaptive fuzzyC-means clustering. By analyzing vibration signal collected, nine common time domain parameters are calculated. This lot of data constitutes data matrix as characteristic vectors to be detected. And using adapti

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Kanzawa, Yuchi. "A Maximizing Model of Bezdek-Like Spherical Fuzzyc-Means." Journal of Advanced Computational Intelligence and Intelligent Informatics 19, no. 5 (2015): 662–69. http://dx.doi.org/10.20965/jaciii.2015.p0662.

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In this study, a maximizing model of Bezdek-like spherical fuzzyc-means clustering is proposed, which is based on the regularization of the maximizing model of spherical hardc-means. Such a maximizing model was unclear in Bezdek-like method, whereas other types of methods have been investigated well both in minimizing and maximizing model. Using theoretical analysis and numerical experiments, the classification rule of the proposed method is shown. Using numerical examples, the proposed method is shown to be valid for document clustering, because documents are represented as spherical data via

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Hamdi, Amira, Nicolas Monmarché, Mohamed Slimane, and Adel M. Alimi. "Fuzzy Rules for Ant Based Clustering Algorithm." Advances in Fuzzy Systems 2016 (2016): 1–16. http://dx.doi.org/10.1155/2016/8198915.

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This paper provides a new intelligent technique for semisupervised data clustering problem that combines the Ant System (AS) algorithm with the fuzzyc-means (FCM) clustering algorithm. Our proposed approach, called F-ASClass algorithm, is a distributed algorithm inspired by foraging behavior observed in ant colonyT. The ability of ants to find the shortest path forms the basis of our proposed approach. In the first step, several colonies of cooperating entities, called artificial ants, are used to find shortest paths in a complete graph that we called graph-data. The number of colonies used in

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Zolkepli, Maslina, Fangyan Dong, and Kaoru Hirota. "Visualizing Fuzzy Relationship in Bibliographic Big Data Using Hybrid Approach Combining Fuzzyc-Means and Newman-Girvan Algorithm." Journal of Advanced Computational Intelligence and Intelligent Informatics 18, no. 6 (2014): 896–907. http://dx.doi.org/10.20965/jaciii.2014.p0896.

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Bibliographic big data visualization method is proposed by incorporating a combination of fuzzyc-means clustering and the Newman-Girvan clustering algorithm, where clustered results are displayed in a network view by grouping objects with similar cluster memberships. As current bibliographic visualizations focus on the crisp relationship among data, fuzzy analysis and visualization may offer insights to bibliographic big data, enabling faster decision making by improving displayed information precision. The proposed method is applied to the DBLP citation network dataset. Results show that merg

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Ye, Mao, Wenfen Liu, Jianghong Wei, and Xuexian Hu. "Fuzzyc-Means and Cluster Ensemble with Random Projection for Big Data Clustering." Mathematical Problems in Engineering 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/6529794.

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Because of its positive effects on dealing with the curse of dimensionality in big data, random projection for dimensionality reduction has become a popular method recently. In this paper, an academic analysis of influences of random projection on the variability of data set and the dependence of dimensions has been proposed. Together with the theoretical analysis, a new fuzzyc-means (FCM) clustering algorithm with random projection has been presented. Empirical results verify that the new algorithm not only preserves the accuracy of original FCM clustering, but also is more efficient than ori

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Ren, Min, Peiyu Liu, Zhihao Wang, and Jing Yi. "A Self-Adaptive Fuzzyc-Means Algorithm for Determining the Optimal Number of Clusters." Computational Intelligence and Neuroscience 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/2647389.

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For the shortcoming of fuzzyc-means algorithm (FCM) needing to know the number of clusters in advance, this paper proposed a new self-adaptive method to determine the optimal number of clusters. Firstly, a density-based algorithm was put forward. The algorithm, according to the characteristics of the dataset, automatically determined the possible maximum number of clusters instead of using the empirical rulenand obtained the optimal initial cluster centroids, improving the limitation of FCM that randomly selected cluster centroids lead the convergence result to the local minimum. Secondly, thi

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YANG, MIIN-SHEN, and KAI FUN YU. "ON STOCHASTIC CONVERGENCE THEOREMS FOR THE FUZZYC-MEANS CLUSTERING PROCEDURE∗." International Journal of General Systems 16, no. 4 (1990): 397–411. http://dx.doi.org/10.1080/03081079008935091.

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Inokuchi, Ryo, and Sadaaki Miyamoto. "Fuzzyc-Means Algorithms Using Kullback-Leibler Divergence and Helliger Distance Based on Multinomial Manifold." Journal of Advanced Computational Intelligence and Intelligent Informatics 12, no. 5 (2008): 443–47. http://dx.doi.org/10.20965/jaciii.2008.p0443.

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In this paper, we discuss fuzzy clustering algorithms for discrete data. Data space is represented as a statistical manifold of the multinomial distribution, and then the Euclidean distance are not adequate in this setting. The geodesic distance on the multinomial manifold can be derived analytically, but it is difficult to use it as a metric directly. We propose fuzzyc-means algorithms using other metrics: the Kullback-Leibler divergence and the Hellinger distance, instead of the Euclidean distance. These two metrics are regarded as approximations of the geodesic distance.

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Endo, Yasunori, Yasushi Hasegawa, Yukihiro Hamasuna, and Yuchi Kanzawa. "Fuzzyc-Means Clustering for Uncertain Data Using Quadratic Penalty-Vector Regularization." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 1 (2011): 76–82. http://dx.doi.org/10.20965/jaciii.2011.p0076.

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Clustering - defined as an unsupervised data-analysis classification transforming real-space information into data in pattern space and analyzing it - may require that data be represented by a set, rather than points, due to data uncertainty, e.g., measurement error margin, data regarded as one point, or missing values. These data uncertainties have been represented as interval ranges for which many clustering algorithms are constructed, but the lack of guidelines in selecting available distances in individual cases has made selection difficult and raised the need for ways to calculate dissimi

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Hamasuna, Yukihiro, Yasunori Endo, and Sadaaki Miyamoto. "Fuzzyc-Means Clustering for Data with Clusterwise Tolerance Based onL2- andL1-Regularization." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 1 (2011): 68–75. http://dx.doi.org/10.20965/jaciii.2011.p0068.

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Detecting various kinds of cluster shape is an important problem in the field of clustering. In general, it is difficult to obtain clusters with different sizes or shapes by single-objective function. From that sense, we have proposed the concept of clusterwise tolerance and constructed clustering algorithms based on it. In the field of data mining, regularization techniques are used in order to derive significant classifiers. In this paper, we propose another concept of clusterwise tolerance from the viewpoint of regularization. Moreover, we construct clustering algorithms for data with clust

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Takumi, Satoshi, and Sadaaki Miyamoto. "Nearest Prototype and Nearest Neighbor Clustering with Twofold Memberships Based on Inductive Property." Journal of Advanced Computational Intelligence and Intelligent Informatics 17, no. 4 (2013): 504–10. http://dx.doi.org/10.20965/jaciii.2013.p0504.

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The aim of this paper is to study methods of twofold membership clustering using the nearest prototype and nearest neighbor. The former uses theK-means, whereas the latter extends the single linkage in agglomerative hierarchical clustering. The concept of inductive clustering is moreover used for the both methods, which means that natural classification rules are derived as the results of clustering, a typical example of which is the Voronoi regions inK-means clustering. When the rule of nearest prototype allocation inK-means is replaced by nearest neighbor classification, we have inductive cl

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Yamamoto, Takeshi, Katsuhiro Honda, Akira Notsu, and Hidetomo Ichihashi. "Non-Euclidean Extension of FCMdd-Based Linear Clustering for Relational Data." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 8 (2011): 1050–56. http://dx.doi.org/10.20965/jaciii.2011.p1050.

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Relational data is common in many real-world applications. Linear fuzzy clustering models have been extended for handling relational data based on Fuzzyc-Medoids (FCMdd) framework. In this paper, with the goal being to handle non-Euclidean data, β-spread transformation of relational data matrices used in Non-Euclidean-type Relational Fuzzy (NERF)c-means is applied before FCMdd-type linear cluster extraction. β-spread transformation modifies data elements to avoid negative values for clustering criteria of distances between objects and linear prototypes. In numerical experiments, typical featur

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L. Lakshmaiah, Et al. "Fuzzy Election based Optimization Algorithm (FEBOA) And Energy Harvesting Possibilistic FUZZYC-Means (EHFPCM) Clustering for EH-WSN." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 3701–13. http://dx.doi.org/10.17762/ijritcc.v11i9.9593.

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Wireless Sensor Network (WSN) includes of many nodes by restricted energy resources. Energy efficiency and harvested energy are major important issues in the WSN. Studies recently conducted have demonstrated that clustering is an effective way to increase energy efficiency. Energy Harvesting- Wireless Sensor Network (EH-WSN) is a flexible strategy for even clustering and Cluster Head (CH) selection is helpful to maximize network constancy and energy efficiency. In this paper, Energy Harvesting Possibilistic Fuzzy C-Means (EHFPCM) clustering is introduced to improve harvested energy usage by ma

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Ubukata, Seiki, Katsuya Koike, Akira Notsu, and Katsuhiro Honda. "MMMs-Induced Possibilistic Fuzzy Co-Clustering and its Characteristics." Journal of Advanced Computational Intelligence and Intelligent Informatics 22, no. 5 (2018): 747–58. http://dx.doi.org/10.20965/jaciii.2018.p0747.

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In the field of cluster analysis, fuzzy theory including the concept of fuzzy sets has been actively utilized to realize flexible and robust clustering methods. FuzzyC-means (FCM), which is the most representative fuzzy clustering method, has been extended to achieve more robust clustering. For example, noise FCM (NFCM) performs noise rejection by introducing a noise cluster that absorbs noise objects and possibilisticC-means (PCM) performs the independent extraction of possibilistic clusters by introducing cluster-wise noise clusters. Similarly, in the field of co-clustering, fuzzy co-cluster

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Chen, Zhijia, Yuanchang Zhu, Yanqiang Di, and Shaochong Feng. "Self-Adaptive Prediction of Cloud Resource Demands Using Ensemble Model and Subtractive-Fuzzy Clustering Based Fuzzy Neural Network." Computational Intelligence and Neuroscience 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/919805.

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In IaaS (infrastructure as a service) cloud environment, users are provisioned with virtual machines (VMs). To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN). We analyze the characters of user preferences and demands. Then the architecture of the prediction model is constructed. We adopt some base predictors to compose the ensemble model. Then the structure and learning al

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Kinoshita, Naohiko, Yasunori Endo, and Yukihiro Hamasuna. "Fuzzyc-Means with Quadratic Penalty-Vector Regularization Using Kullback-Leibler Information for Uncertain Data." Journal of Advanced Computational Intelligence and Intelligent Informatics 19, no. 5 (2015): 624–31. http://dx.doi.org/10.20965/jaciii.2015.p0624.

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Clustering, a highly useful unsupervised classification, has been applied in many fields. When, for example, we use clustering to classify a set of objects, it generally ignores any uncertainty included in objects. This is because uncertainty is difficult to deal with and model. It is desirable, however, to handle individual objects as is so that we may classify objects more precisely. In this paper, we propose new clustering algorithms that handle objects having uncertainty by introducing penalty vectors. We show the theoretical relationship between our proposal and conventional algorithms ve

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Kanzawa, Yuchi, Yasunori Endo, and Sadaaki Miyamoto. "Semi-Supervised Fuzzyc-Means Algorithm by Revising Dissimilarity Between Data." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 1 (2011): 95–101. http://dx.doi.org/10.20965/jaciii.2011.p0095.

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We propose two approaches for semi-supervised FCM withsoftpairwise constraints. One applies NERFCM to the revised dissimilarity matrix by pairwise constraints. The other applies K-FCM with a dissimilarity-based kernel function, revising the dissimilarity matrix based on whether data in the same cluster may be close to each other or the data in the different clusters may be apart from each other. Propagating given pairwise constraints to unconstrained data is done when given constraints are not sufficient to obtain the desired clustering result. Numerical examples show that the proposed algorit

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Dubey, Yogita K., and Milind M. Mushrif. "FCM Clustering Algorithms for Segmentation of Brain MR Images." Advances in Fuzzy Systems 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/3406406.

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The study of brain disorders requires accurate tissue segmentation of magnetic resonance (MR) brain images which is very important for detecting tumors, edema, and necrotic tissues. Segmentation of brain images, especially into three main tissue types: Cerebrospinal Fluid (CSF), Gray Matter (GM), and White Matter (WM), has important role in computer aided neurosurgery and diagnosis. Brain images mostly contain noise, intensity inhomogeneity, and weak boundaries. Therefore, accurate segmentation of brain images is still a challenging area of research. This paper presents a review of fuzzyc-mean

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Jingxiong, Zhang, and Roger Kirby. "An improved algorithm for supervised fuzzyC-means clustering of remotely sensed data." Geo-spatial Information Science 3, no. 1 (2000): 39–44. http://dx.doi.org/10.1007/bf02826805.

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Sun, Jiajia, and Yaoguo Li. "Multidomain petrophysically constrained inversion and geology differentiation using guided fuzzyc-means clustering." GEOPHYSICS 80, no. 4 (2015): ID1—ID18. http://dx.doi.org/10.1190/geo2014-0049.1.

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Ergen, Burhan. "A Fusion Method of Gabor Wavelet Transform and Unsupervised Clustering Algorithms for Tissue Edge Detection." Scientific World Journal 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/964870.

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This paper proposes two edge detection methods for medical images by integrating the advantages of Gabor wavelet transform (GWT) and unsupervised clustering algorithms. The GWT is used to enhance the edge information in an image while suppressing noise. Following this, thek-means and Fuzzyc-means (FCM) clustering algorithms are used to convert a gray level image into a binary image. The proposed methods are tested using medical images obtained through Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) devices, and a phantom image. The results prove that the proposed methods are succ

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YANG, MIIN-SHEN, and KAI FUN YU. "ON EXISTENCE AND STRONG CONSISTENCY OF A CLASS OF FUZZYC-MEANS CLUSTERING PROCEDURES." Cybernetics and Systems 23, no. 6 (1992): 583–602. http://dx.doi.org/10.1080/01969729208927483.

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Elazab, Ahmed, Changmiao Wang, Fucang Jia, Jianhuang Wu, Guanglin Li, and Qingmao Hu. "Segmentation of Brain Tissues from Magnetic Resonance Images Using Adaptively Regularized Kernel-Based FuzzyC-Means Clustering." Computational and Mathematical Methods in Medicine 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/485495.

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An adaptively regularized kernel-based fuzzyC-means clustering framework is proposed for segmentation of brain magnetic resonance images. The framework can be in the form of three algorithms for the local average grayscale being replaced by the grayscale of the average filter, median filter, and devised weighted images, respectively. The algorithms employ the heterogeneity of grayscales in the neighborhood and exploit this measure for local contextual information and replace the standard Euclidean distance with Gaussian radial basis kernel functions. The main advantages are adaptiveness to loc

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Cui, Wenchao, Yi Wang, Yangyu Fan, Yan Feng, and Tao Lei. "Localized FCM Clustering with Spatial Information for Medical Image Segmentation and Bias Field Estimation." International Journal of Biomedical Imaging 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/930301.

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This paper presents a novel fuzzy energy minimization method for simultaneous segmentation and bias field estimation of medical images. We first define an objective function based on a localized fuzzyc-means (FCM) clustering for the image intensities in a neighborhood around each point. Then, this objective function is integrated with respect to the neighborhood center over the entire image domain to formulate a global fuzzy energy, which depends on membership functions, a bias field that accounts for the intensity inhomogeneity, and the constants that approximate the true intensities of the c

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Ma, Li, Yang Li, Suohai Fan, and Runzhu Fan. "A Hybrid Method for Image Segmentation Based on Artificial Fish Swarm Algorithm and Fuzzyc-Means Clustering." Computational and Mathematical Methods in Medicine 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/120495.

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Image segmentation plays an important role in medical image processing. Fuzzyc-means (FCM) clustering is one of the popular clustering algorithms for medical image segmentation. However, FCM has the problems of depending on initial clustering centers, falling into local optimal solution easily, and sensitivity to noise disturbance. To solve these problems, this paper proposes a hybrid artificial fish swarm algorithm (HAFSA). The proposed algorithm combines artificial fish swarm algorithm (AFSA) with FCM whose advantages of global optimization searching and parallel computing ability of AFSA ar

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Qiao, Yu-Long, Kai-Long Yuan, Chun-Yan Song, and Xue-Zhi Xiang. "Detection of Moving Objects with Fuzzy Color Coherence Vector." Mathematical Problems in Engineering 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/138065.

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Background subtraction is a popular method for detecting foreground that is widely adopted as the fundamental processing for advanced applications such as tracking and surveillance. Color coherence vector (CCV) includes both the color distribution information (histogram) and the local spatial relationship information of colors. So it overcomes the weakness of the conventional color histogram for the representation of an object. In this paper, we introduce a fuzzy color coherence vector (FCCV) based background subtraction method. After applying the fuzzyc-means clustering to color coherence sub

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Endo, Yasunori, Tomoyuki Suzuki, Naohiko Kinoshita, and Yukihiro Hamasuna. "On Fuzzy Non-Metric Model for Data with Tolerance and its Application to Incomplete Data Clustering." Journal of Advanced Computational Intelligence and Intelligent Informatics 20, no. 4 (2016): 571–79. http://dx.doi.org/10.20965/jaciii.2016.p0571.

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The fuzzy non-metric model (FNM) is a representative non-hierarchical clustering method, which is very useful because the belongingness or the membership degree of each datum to each cluster can be calculated directly from the dissimilarities between data and the cluster centers are not used. However, the original FNM cannot handle data with uncertainty. In this study, we refer to the data with uncertainty as “uncertain data,” e.g., incomplete data or data that have errors. Previously, a methods was proposed based on the concept of a tolerance vector for handling uncertain data and some cluste

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Sukim, Sukim, Firdaus Firdaus, Retnaningsih Retnaningsih, and Efri Diah Utami. "Mengukur Kepemimpinan Perempuan di Indonesia dengan Metode Fuzzy c-Means Clustering." STATISTIKA: Journal of Theoretical Statistics and Its Applications 18, no. 2 (2019): 101–12. http://dx.doi.org/10.29313/jstat.v18i2.4536.

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Indonesia is fully committed to implement Sustainable Development Goals (SDGs). The goal 5 ofSDGs priorities the need to end discrimination against women and girls in all forms, and meetingtheir right to equal opportunities in employment, health and education. It is in line with thePresidential Instruction No. 9/2000 on gender mainstreaming in the National development programs.According to the result of the 2015 Intercensal Population census, about 49.75 percent of 255.18million Indonesian population are women. This large figure population of women could be an assetfor the national development

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Kumar, Pramod, and Ashvini Chaturvedi. "Probabilistic query generation and fuzzyc-means clustering for energy-efficient operation in wireless sensor networks." International Journal of Communication Systems 29, no. 8 (2016): 1439–50. http://dx.doi.org/10.1002/dac.3112.

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Kwong, C. K., K. Y. Fung, Huimin Jiang, K. Y. Chan, and Kin Wai Michael Siu. "A Modified Dynamic Evolving Neural-Fuzzy Approach to Modeling Customer Satisfaction for Affective Design." Scientific World Journal 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/636948.

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Affective design is an important aspect of product development to achieve a competitive edge in the marketplace. A neural-fuzzy network approach has been attempted recently to model customer satisfaction for affective design and it has been proved to be an effective one to deal with the fuzziness and non-linearity of the modeling as well as generate explicit customer satisfaction models. However, such an approach to modeling customer satisfaction has two limitations. First, it is not suitable for the modeling problems which involve a large number of inputs. Second, it cannot adapt to new data

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Zhang, Dawei, Fuding Xie, Dapeng Wang, Yong Zhang, and Yan Sun. "Cluster Analysis Based on Bipartite Network." Mathematical Problems in Engineering 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/676427.

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Clustering data has a wide range of applications and has attracted considerable attention in data mining and artificial intelligence. However it is difficult to find a set of clusters that best fits natural partitions without any class information. In this paper, a method for detecting the optimal cluster number is proposed. The optimal cluster number can be obtained by the proposal, while partitioning the data into clusters by FCM (Fuzzyc-means) algorithm. It overcomes the drawback of FCM algorithm which needs to define the cluster numbercin advance. The method works by converting the fuzzy c

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Lou, Jian-Yong, Xu-Lei Yang, and Ai-Ze Cao. "A Spatial Shape Constrained Clustering Method for Mammographic Mass Segmentation." Computational and Mathematical Methods in Medicine 2015 (2015): 1–16. http://dx.doi.org/10.1155/2015/891692.

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A novel clustering method is proposed for mammographic mass segmentation on extracted regions of interest (ROIs) by using deterministic annealing incorporating circular shape function (DACF). The objective function reported in this study uses both intensity and spatial shape information, and the dominant dissimilarity measure is controlled by two weighting parameters. As a result, pixels having similar intensity information but located in different regions can be differentiated. Experimental results shows that, by using DACF, the mass segmentation results in digitized mammograms are improved w

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Alomari, Yazan M., Siti Norul Huda Sheikh Abdullah, Reena Rahayu MdZin, and Khairuddin Omar. "Adaptive Localization of Focus Point Regions via Random Patch Probabilistic Density from Whole-Slide, Ki-67-Stained Brain Tumor Tissue." Computational and Mathematical Methods in Medicine 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/673658.

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Analysis of whole-slide tissue for digital pathology images has been clinically approved to provide a second opinion to pathologists. Localization of focus points from Ki-67-stained histopathology whole-slide tissue microscopic images is considered the first step in the process of proliferation rate estimation. Pathologists use eye pooling or eagle-view techniques to localize the highly stained cell-concentrated regions from the whole slide under microscope, which is called focus-point regions. This procedure leads to a high variety of interpersonal observations and time consuming, tedious wor

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Kabbur, Mahabaleshwar S. "An Efficient Multiclass Medical Image CBIR System Based on Classification and Clustering." Journal of Intelligent Systems 27, no. 2 (2018): 275–90. http://dx.doi.org/10.1515/jisys-2016-0156.

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AbstractIn this paper, we are going to present the multiclass medical image content-based image retrieval (CBIR) system based on classification and clustering. Images are segmented using hill climbing-based segmentation (HCBS) based on the extracted visual features. In the improved HCBS technique, a clustering that is based on kernel-based fuzzyC-means is employed. In the next step, features like color, texture, edge density, region area, and visual words from the segmented images are extracted. The visual word can be extracted by using the clustering techniques. This visual word represents th

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Yasuda, Makoto. "Approximate Determination ofq-Parameter for FCM with Tsallis Entropy Maximization." Journal of Advanced Computational Intelligence and Intelligent Informatics 21, no. 7 (2017): 1152–60. http://dx.doi.org/10.20965/jaciii.2017.p1152.

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This paper considers a fuzzyc-means (FCM) clustering algorithm in combination with deterministic annealing and the Tsallis entropy maximization. The Tsallis entropy is aq-parameter extension of the Shannon entropy. By maximizing the Tsallis entropy within the framework of FCM, statistical mechanical membership functions can be derived. One of the major considerations when using this method is how to determine appropriate values forqand the highest annealing temperature,Thigh, for a given data set. Accordingly, in this paper, a method for determining these values simultaneously without introduc

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