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Fuzzy clustering with volume prototypes and adaptive cluster merging

U. Kaymak, M. Setnes
2002 IEEE transactions on fuzzy systems  
His interests include fuzzy systems and computational intelligence techniques for modeling, control, and decision making.  ...  ACKNOWLEDGMENT The authors would like to thank the anonymous referees for their comments and valuable suggestions.  ...  EXTENDED GK AND FCM ALGORITHMS In this section, we give an algorithm for the extended fuzzy c-means (E-FCM) and the extended GK (E-GK) clustering.  ... 
doi:10.1109/tfuzz.2002.805901 fatcat:xx42d245dvaldkhpplpxko3lba

Segmentation of MRI Brain Images with an Improved Harmony Searching Algorithm

Zhang Yang, Ye Shufan, Guo Li, Ding Weifeng
2016 BioMed Research International  
The optimal value of convergence was employed as the initial value of the fuzzy clustering algorithm for segmenting magnetic resonance imaging (MRI) brain images.  ...  In our study, the MRI image segmentation effect of the improved algorithm was superior to that of the original fuzzy clustering method.  ...  Acknowledgments This work is supported by Scientific Research Task in the Department of Education of Zhejiang (Y201328002) and Talent Starting Task of Wenzhou Medical University (QTJ11008).  ... 
doi:10.1155/2016/4516376 pmid:27403428 pmcid:PMC4926041 fatcat:ptvjlvi4czdktavuie7z4ko2ba

Integrating Fuzzy c-Means Clustering with PostgreSQL

R. M. Miniakhmetov
2018 Proceedings of the Institute for System Programming of RAS  
In this paper we propose Fuzzy c-Means clustering algorithm adapted for PostgreSQL open-source relational DBMS.  ...  Having a clusterization algorithm implemented in SQL provides easier clusterization inside a relational DBMS than outside with some alternative tools.  ...  The Fuzzy c-Means (FCM) [2] , [3] , [4] clustering algorithm provides a fuzzy clustering of data.  ... 
doaj:441e350ae227495dbfbdf11c5e10f577 fatcat:nkr55y7gg5a6vafrsgwbdfm2ai

Generating Clustering-Based Interval Fuzzy Type-2 Triangular and Trapezoidal Membership Functions: A Structured Literature Review

Siti Hajar Khairuddin, Mohd Hilmi Hasan, Manzoor Ahmed Hashmani, Muhammad Hamza Azam
2021 Symmetry  
The methods imply flexibility in choosing membership function type, hence increasing the effectiveness of fuzzy applications through leveraging the advantages that each of the three membership function  ...  To ensure that the review also covers the important components of fuzzy logic, this paper also reviews and discusses another 49 manuscripts on fuzzy calculation and operation.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/sym13020239 fatcat:tggp4e2zifd6jggtkfid3oq5em

A Fuzzy C-Means Clustering Algorithm Based on Improved Quantum Genetic Algorith

An-Xin Ye, Yong-Xian Jin
2016 International Journal of Database Theory and Application  
Aiming at the problem of traditional fuzzy C-means clustering algorithm that it is sensitive to the initial clustering centers and easy to fall into the local optimization, an improved algorithm that combines  ...  Improved Quantum Genetic Optimization with FCM algorithm is proposed.  ...  However, FCM has some shortcomings that have motivated the proposal of alternative approaches for fuzzy clustering, many of which are extensions of FCM.  ... 
doi:10.14257/ijdta.2016.9.1.20 fatcat:heqypn2nlfah5e3ovwyjptgp5a

Implementation of the Fuzzy C-Means Clustering Algorithm in Meteorological Data

Yinghua Lu, Tinghuai Ma, Changhong Yin, Xiaoyu Xie, Wei Tian, Shui Ming Zhong
2013 International Journal of Database Theory and Application  
The algorithm is an extension of the classical and the crisp k-means clustering method in fuzzy set domain.  ...  Among the fuzzy clustering method, the fuzzy c-means (FCM) algorithm [9] is the most well-known method because it has the advantage of robustness for ambiguity and maintains much more information than  ...  Acknowledgements This work was supported in part by National Science Foundation of China (No. 61173143), China Postdoctoral Science Foundation (No.2012M511783), also was supported by Qing Lan Project of  ... 
doi:10.14257/ijdta.2013.6.6.01 fatcat:wfaon726frf3feup6jziw3qine

Linear Fuzzy Clustering Techniques With Missing Values and Their Application to Local Principal Component Analysis

K. Honda, H. Ichihashi
2004 IEEE transactions on fuzzy systems  
One is an extension of fuzzy -varieties clustering that can be regarded as the algorithm for the local principal component analysis of fuzzy covariance matrices.  ...  Numerical examples show that the methods provide useful tools for interpretation of the local structures of a database. Index Terms-Fuzzy clustering, missing value, principal component analysis.  ...  ACKNOWLEDGMENT The authors would like to thank the anonymous reviewers for their valuable comments.  ... 
doi:10.1109/tfuzz.2004.825073 fatcat:pb4s7djlrjdnnpylggm74ezjse

A new ECG beat clustering method based on kernelized fuzzy c-means and hybrid ant colony optimization for continuous domains

Berat Doğan, Mehmet Korürek
2012 Applied Soft Computing  
The kernelized fuzzy c-means algorithm uses kernel methods to improve the clustering performance of the well known fuzzy c-means algorithm by mapping a given dataset into a higher dimensional space non-linearly  ...  However, to further improve the clustering performance, an optimization method is required to overcome the drawbacks of the traditional algorithms such as, sensitivity to initialization, trapping into  ...  One of these fuzzy clustering algorithms is the fuzzy c-means (FCM) algorithm [2] .  ... 
doi:10.1016/j.asoc.2012.07.007 fatcat:uqnqsdhm2rgyfgvqbb4f7izifu

Agent Based Segmentation of the MRI Brain Using a Robust C-Means Algorithm

Hanane Barrah, Abdeljabbar Cherkaoui, Driss Sarsri
2016 Journal of Computer and Communications  
Because of the fuzzy nature of the MRI images, many researchers have adopted the fuzzy clustering approach to segment them.  ...  In this work, a fast and robust multi-agent system (MAS) for MRI segmentation of the brain is proposed.  ...  Standard Fuzzy C-Means Algorithm: FCM C-means is the best-known fuzzy clustering algorithm that is based on the fuzzy sets theory [24] to create homogeneous clusters.  ... 
doi:10.4236/jcc.2016.410002 fatcat:pf7nfnu3kjayroiewafggjxogi

Processing Imprecise Database Queries by Fuzzy Clustering Algorithms

Anna Kowalczyk-Niewiadomy, Adam Pelikant
2015 Position Papers of the 2015 Federated Conference on Computer Science and Information Systems  
The basic idea of presented research is to extend an existing query language and make database systems able to satisfy user needs more closely.  ...  Nowadays database management systems are one of the most critical resources in every company.  ...  Fuzzy C-Medoids Clustering (FCMdd) Fuzzy C-Medoids Clustering [8] , relies on the basic idea of Fuzzy C-means clustering (FCM) with the difference of calculating cluster centers.  ... 
doi:10.15439/2015f1 dblp:conf/fedcsis/Kowalczyk-Niewiadomy15 fatcat:jax7z4sutzd77npg2ntej3tvom

Robust Implementation of ALFIS for Prediction of Medical Information System

M. Sindhu, S. Venkatesh, A. Mary Benita
2012 International Journal of Computer Applications  
In this paper we proposed a fuzzy logical network that enhances the learning ability of FCM.  ...  The effectiveness of the proposed approach in prediction of jaundice using clustering is demonstrated through numerical simulation. in FCM.  ...  Fuzzy clustering can be considered the most important unsupervised learning algorithm and fuzzy cmean is the most popular fuzzy clustering method among different fuzzy clustering algorithms.  ... 
doi:10.5120/9418-3251 fatcat:3f5kqen6qjg4hlruz6tvpmgfxq

A Comparative Analysis of MRI Brain Tumor Segmentation Technique

Anubha Lakra, R.B. Dubey
2015 International Journal of Computer Applications  
This paper presents a performance analysis of image segmentation techniques, viz., Genetic algorithm, K-Means Clustering and Fuzzy C-Means clustering for detection of brain tumor from brain MRI images.  ...  General Terms Segmentation algorithms, Brain tumor Keywords MRI brain tumor, segmentation, Genetic algorithm, K-means clustering and Fuzzy C-means clustering.  ...  FCM clustering is performed using fuzzy logic Toolbox. FCM start with an assumption for the cluster enters, which are used to mark the location for mean of each cluster.  ... 
doi:10.5120/ijca2015905922 fatcat:sm2s5g23pbayjok5ojphqliilq

Fuzzy c-Means Algorithms for Very Large Data

T. C. Havens, J. C. Bezdek, C. Leckie, L. O. Hall, M. Palaniswami
2012 IEEE transactions on fuzzy systems  
Clustering is one of the primary tasks used in the pattern recognition and data mining communities to search VL databases (including VL images) in various applications, and so, clustering algorithms that  ...  This paper compares the efficacy of three different implementations of techniques aimed to extend fuzzy c-means (FCM) clustering to VL data.  ...  Perhaps the most well-known method for fuzzy clustering of VL data is the generalized extensible fast FCM (geFFCM) [12] .  ... 
doi:10.1109/tfuzz.2012.2201485 fatcat:dwzriaqdijg4dc2jndhblbx5lm

Multiple Kernel Fuzzy Clustering

Hsin-Chien Huang, Yung-Yu Chuang, Chu-Song Chen
2012 IEEE transactions on fuzzy systems  
Kernel combination, or selection, is crucial for effective kernel clustering. Unfortunately, for most applications, it is uneasy to find the right combination.  ...  We propose a multiple kernel fuzzy c-means (MKFC) algorithm that extends the fuzzy c-means algorithm with a multiple kernel-learning setting.  ...  This class of clustering methods is called soft-or fuzzy-clustering. Fuzzy c-means (FCM) [7] , [8] is one of the most promising fuzzy clustering methods.  ... 
doi:10.1109/tfuzz.2011.2170175 fatcat:u4qtgpm4mnfo7py3enqvzh7wfu

Scalability and Fuzzy Systems: What Parallelization Can Do [chapter]

Malaquias Q. Flores, Federico Del Razo, Anne Laurent, Nicolas Sicard
2013 Studies in Computational Intelligence  
More precisely, we present the parallelization of fuzzy database mining algorithms on multi-core architectures of four knowledge discovery paradigms, namely fuzzy association rules, fuzzy clustering, fuzzy  ...  In this paper, we discuss how the parallelization of fuzzy algorithms is crucial to tackle the problem of scalability and optimal performance in the context of database mining.  ...  The most widely used fuzzy clustering algorithm is the Fuzzy c-Means (FCM) algorithm proposed by Dunn and generalised by Bezdek.  ... 
doi:10.1007/978-3-319-00954-4_13 fatcat:okkfilr5njdmrbijkdeyryuqca
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