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Fuzzy clustering with volume prototypes and adaptive cluster merging
2002
IEEE transactions on fuzzy systems
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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
2016
BioMed Research International
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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
2018
Proceedings of the Institute for System Programming of RAS
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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
2021
Symmetry
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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
2016
International Journal of Database Theory and Application
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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
2013
International Journal of Database Theory and Application
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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
2004
IEEE transactions on fuzzy systems
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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
2012
Applied Soft Computing
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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
2016
Journal of Computer and Communications
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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
2015
Position Papers of the 2015 Federated Conference on Computer Science and Information Systems
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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
2012
International Journal of Computer Applications
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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
2015
International Journal of Computer Applications
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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
2012
IEEE transactions on fuzzy systems
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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
2012
IEEE transactions on fuzzy systems
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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]
2013
Studies in Computational Intelligence
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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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