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Assimilated Strong Fuzzy C-means in MR Images for Glioblastoma Multiforme
2015
Indian Journal of Science and Technology
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Methods/Statistical Analysis: This automated process implemented by a robust Fuzzy C-Means (FCM). This FCM needs novel objective function. This is obtained by performing replacement. ...
The initial cluster reduces both the running time and computational complexity. The synthetic image with benchmark dataset used to perform initial experimental work. ...
Therefore, the new novel Fuzzy C-Means leads to cluster complex noised dataset of image into more appropriate groups. ...
doi:10.17485/ijst/2015/v8i1/79281
fatcat:5khggotu7jffrnhlnuwrhs7mri
Fuzzy encoding for image classification using Gustafson-Kessel algorithm
2012
2012 19th IEEE International Conference on Image Processing
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We use the Gustafson-Kessel algorithm which is an improvement over Fuzzy C-Means clustering and can adapt to local distributions. ...
This paper presents a novel adaptation of fuzzy clustering and feature encoding for image classification. ...
In this paper, we present a novel adaptation of the Gustafson-Kessel (GK) algorithm [2] for building a fuzzy visual codebook. ...
doi:10.1109/icip.2012.6467565
dblp:conf/icip/GuptaB12
fatcat:6a2yy4ssnjbvjlr4ulxdzna534
Semi-Supervised Fuzzy Clustering with Feature Discrimination
2015
PLoS ONE
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By taking pairwise constraints into account, we propose a semi-supervised fuzzy clustering algorithm with feature discrimination (SFFD) incorporating a fully adaptive distance function. ...
In order to make the clustering approach useful and acceptable to users, the information provided must be simple, natural and limited in number. ...
Acknowledgments We wish to thank Meili Wang for her suggestion in improving algorithm performances during the development and deployment stage.
Author Contributions ...
doi:10.1371/journal.pone.0131160
pmid:26325272
pmcid:PMC4556708
fatcat:jaduhug6wjdnjkvd3donwcmkse
Gaussian Kernelized Fuzzy c-means with Spatial Information Algorithm for Image Segmentation
2012
Journal of Computers
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In this paper, Gaussian kernel-based fuzzy c-means algorithm with spatial information (KSFCM) is proposed. KSFCM is more robust and adaptive. ...
FCM is used for image segmentation in some applications. It is based on a specific distance norm and does not use spatial information of the image, so it has some drawbacks. ...
Clustering is a popular method used for image segmentation for its simplicity and easiness to implement. The commonly used clustering algorithms are hard cmeans and the fuzzy c-means algorithm. ...
doi:10.4304/jcp.7.6.1511-1518
fatcat:e5f7ycyfsjdzjb3khtxelxpcja
Introduction of Local Spatial Constraints and Local Similarity Estimation in Possibilistic c-Means Algorithm for Remotely Sensed Imagery
2019
Journal of Modeling and Optimization
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This paper presents a unique Possibilistic c-Means with constraints (PCM-S) with Adaptive Possibilistic Local Information c-Means (ADPLICM) in a supervised way by incorporating local information through ...
PCM-S with ADPLICM overcome the limitations of the known Possibilistic c-Means (PCM) and Possibilistic c-Means with constraints (PCM-S) algorithms. ...
Mathematical concept of PCM-S with ADPLICM algorithm Zhang et al [17] have used Local Similarity Measure based on Pixel Spatial Attraction Model in Adaptive Fuzzy Local Information c-means which adaptively ...
doi:10.32732/jmo.2019.11.1.51
fatcat:voxfdzm5a5ecpenzi42epazlri
"Data Clustering Approach to Industrial Process Monitoring, Fault Detection and Isolation"
2011
International Journal of Computer Applications
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This paper takes a case study of plant data and implements K means algorithm and fuzzy C means algorithm to cluster the relevant data. ...
This paper also discusses the comparison for K means algorithm and fuzzy C means algorithm. ...
It gives a comparative study of clustering using K means algorithm and fuzzy C means algorithm. ...
doi:10.5120/2189-2777
fatcat:5xykfrupizfhvj3jmoip42fmaq
A Novel Modified Adaptive Fuzzy Inference Engine And Its Application To Pattern Classification
2011
Zenodo
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A TSK type fuzzy inference system is constructed by the automatic generation of membership functions and rules by the fuzzy c-means clustering and Apriori algorithm technique, respectively. ...
The present paper proposes a novel Modified Adaptive Fuzzy Inference Engine (MAFIE) for pattern classification. ...
The Fuzzy C-Means (FCM) clustering algorithm [9] is used to find each cluster adaptively. ...
doi:10.5281/zenodo.1078595
fatcat:4tn7stcgoreypbxpu2fefeyt7m
Kernelized Fuzzy C-means Clustering with Adaptive Thresholding for Segmenting Liver Tumors
2016
Procedia Computer Science
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The KFCM algorithm introduces a kernel function on fuzzy c-means clustering (FCM) to reduce the effect of noise and improves the ability of clustering. ...
In this paper, the adaptive threshold, morphological processing, and kernel fuzzy C-means (KFCM) clustering algorithm have been used with spatial information for visualizing and measuring the tumor area ...
using Kernelized Fuzzy C Means
Extraction of tumor using Fuzzy C Means Calculation of ROI p ( ) minimized by updating the cluster centroid iteratively. ...
doi:10.1016/j.procs.2016.07.395
fatcat:ijjscy62ybdbfcymwav52f2ukq
2020 Index IEEE Transactions on Fuzzy Systems Vol. 28
2020
IEEE transactions on fuzzy systems
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., An Optimized Type-2 Self-Organizing Fuzzy Logic Controller Applied in Anesthesia for Propofol Dosing to Regulate BIS; TFUZZ June 2020 1062-1072 Weinstein, A., see Veloz, A., TFUZZ Jan. 2020 100-111 ...
Wen, S., see Liu, S., 1329-1343 Wen, S., see Xiao, B., TFUZZ Dec. 2020 3171-3180 Williams, H., see 2691-2701 Wong, P.K., see 2277-2284 Wozniak, M., see Capizzi, G., TFUZZ June 2020 1178-1189 Wu, C. ...
Hua, C., +, An Edge-Cloud-Aided High-Order Possibilistic c-Means Algorithm for Big Data Clustering. ...
doi:10.1109/tfuzz.2020.3048828
fatcat:vml5fun6szcqbhpceebk3xfg2u
A New ANFIS Model based on Multi-Input Hamacher T-norm and Subtract Clustering
2014
Open Cybernetics and Systemics Journal
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This paper proposed a novel adaptive neuro-fuzzy inference system (ANFIS), which combines subtract clustering, employs adaptive Hamacher T-norm and improves the prediction ability of ANFIS. ...
The expression of multi-input Hamacher T-norm and its relative feather has been originally given, which supports the operation of the proposed system. ...
would like to thank for the support by innovation Project of Guangxi Graduate Education (YCSZ2014203), Project of Outstanding Young Teachers' Training in Higher Education Institutions of Guangxi and a ...
doi:10.2174/1874110x01408010829
fatcat:qngitroy4facjjoycxapwzrhji
Kohonen Maps Combined to Fuzzy C-means, a Two Level Clustering Approach. Application to Electricity Load Data
[chapter]
2011
Self Organizing Maps - Applications and Novel Algorithm Design
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These prototypes are then clustered in the second abstraction level using the fuzzy c-means clustering algorithm (Section 3). ...
In each training 542 Self Organizing Maps -Applications and Novel Algorithm Design www.intechopen.com Kohonen Maps Combined to Fuzzy C-means, a Two Level Clustering Approach. ...
Kohonen Maps Combined to Fuzzy C-means, a Two Level Clustering Approach. ...
doi:10.5772/14206
fatcat:5jvztdbgivgkxmhijaqv5ce56y
Role of Support Vector Machine Fuzzy KMeans and Naive Bayes Classification in Intrusion Detection System
2015
International Journal on Recent and Innovation Trends in Computing and Communication
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For detecting any Intrusion in a network or system there are number of techniques which are used and can be developed to prevent. ...
An Intrusion Detection system is used to detect and monitor the number of happenings and episode in a network or a system. It will gather the information and analyse that information. ...
Unsupervised Learning Algorithms 1. k-Means Clustering -k-Means clustering is a classical clustering algorithm [8] . ...
doi:10.17762/ijritcc2321-8169.150346
fatcat:apv4ccpz35gtpnlah3t63ub3vi
An Island Grouping Genetic Algorithm for Fuzzy Partitioning Problems
2014
The Scientific World Journal
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Our approach hinges on a GGA devised for fuzzy clustering by means of a novel encoding of individuals (containing elements and clusters sections), a new fitness function (a superior modification of the ...
This paper presents a novel fuzzy clustering technique based on grouping genetic algorithms (GGAs), which are a class of evolutionary algorithms especially modified to tackle grouping problems. ...
achieved by the fuzzy C-means algorithm. 2.2.1. ...
doi:10.1155/2014/916371
pmid:24977235
pmcid:PMC4055530
fatcat:cspajcgxdbdx5fa5japajfyw5i
Image segmentation based on adaptive cluster prototype estimation
2005
IEEE transactions on fuzzy systems
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An image segmentation algorithm based on adaptive fuzzy c-means (FCM) clustering is presented in this paper. ...
By introducing a novel dissimilarity index in the modified FCM objective function, the new adaptive fuzzy clustering algorithm is capable of utilizing local contextual information to impose local spatial ...
One popular technique involves using the fuzzy c-means (FCM) algorithm [7] , or variants of it, to compute the membership values for different classes before the final segmentation. ...
doi:10.1109/tfuzz.2004.841748
fatcat:b5hrryn545arvl23x7kvv7qxtu
A Novel Selective Scale Space based Fuzzy C-means Model for Spatial Clustering
2013
Procedia Technology - Elsevier
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This paper proposed a novel Scale Space Filter based Fuzzy C-Means algorithm for clustering spatial data. The number of clusters, C , in present case is known in advance. ...
The Xie-Beni validity index is used as Objective Function of the model to check the quality of the clusters produced. The Results are tested on Standard iris data. ...
Given such information, the generic Fuzzy C-Means technique is stated using algorithm 1.
Algorithm 1 Fuzzy C-Means Algorithm Input: S = {p 1 , p 2 , ··· , p n } Points to be clustered. ...
doi:10.1016/j.protcy.2013.12.400
fatcat:p2scijrvrrap5jfafcgoddbamm
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