scholarly journals Evaluation of FCV and FCM clustering algorithms in cluster-based compound selection

2011 ◽  
Author(s):  
Sinarwati Mohamad Suhaili ◽  
Mohamad Nazim Jambli ◽  
Sharin Hazlin Huspi
Keyword(s):  
Clustering Algorithms ◽  
Fcm Clustering ◽  
Compound Selection

scholarly journals A Bi-directional Fuzzy C-Means Clustering Ensemble Algorithm Considering Local Information

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Chunhua Ren ◽  
Linfu Sun
Keyword(s):  
Clustering Algorithms ◽  
Real Data ◽  
Local Information ◽  
Data Sets ◽  
Clustering Ensemble ◽  
K Nearest Neighbors ◽  
Fuzzy C Means ◽  
Clustering Quality ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering

AbstractThe classic Fuzzy C-means (FCM) algorithm has limited clustering performance and is prone to misclassification of border points. This study offers a bi-directional FCM clustering ensemble approach that takes local information into account (LI_BIFCM) to overcome these challenges and increase clustering quality. First, various membership matrices are created after running FCM multiple times, based on the randomization of the initial cluster centers, and a vertical ensemble is performed using the maximum membership principle. Second, after each execution of FCM, multiple local membership matrices of the sample points are created using multiple K-nearest neighbors, and a horizontal ensemble is performed. Multiple horizontal ensembles can be created using multiple FCM clustering. Finally, the final clustering results are obtained by combining the vertical and horizontal clustering ensembles. Twelve data sets were chosen for testing from both synthetic and real data sources. The LI_BIFCM clustering performance outperformed four traditional clustering algorithms and three clustering ensemble algorithms in the experiments. Furthermore, the final clustering results has a weak correlation with the bi-directional cluster ensemble parameters, indicating that the suggested technique is robust.

A Study of FCM Clustering Algorithm Based on Interval Multiple Attribute Information

2013 ◽  
Vol 444-445 ◽  
pp. 676-680
Author(s):  
Li Guo ◽  
Guo Feng Liu ◽  
Yu E Bao
Keyword(s):  
Clustering Algorithm ◽  
Distance Measure ◽  
Clustering Algorithms ◽  
Interval Numbers ◽  
Fuzzy Partition ◽  
Multiple Attribute ◽  
Attribute Information ◽  
Fcm Clustering ◽  
Attribute Clustering ◽  
Interval Valued

In multiple attribute clustering algorithms with uncertain interval numbers, most of the distances between the interval-valued vectors only consider the differences of each interval endpoint ignoring a lot of information. To solve this problem, according to the differences between corresponding points in each interval number, this paper gives a distance formula between interval-valued vectors, extends a FCM clustering algorithm based on interval multiple attribute information. Through an example, we prove the validity and rationality of the algorithm. Keywords: interval-valued vector; FCM clustering algorithm; distance measure; fuzzy partition

scholarly journals The Cascaded Enhanced k-Means and Fuzzy c-Means Clustering Algorithms for Automated Segmentation of Malaria Parasites

2018 ◽  
Vol 150 ◽  
pp. 06037 ◽  
Author(s):  
Aimi Salihah Abdul Nasir ◽  
Haryati Jaafar ◽  
Wan Azani Wan Mustafa ◽  
Zeehaida Mohamed
Keyword(s):  
Sensitivity And Specificity ◽  
Clustering Algorithms ◽  
Malaria Diagnosis ◽  
World Health ◽  
Segmentation Accuracy ◽  
Efficient Control ◽  
Segmentation Approach ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering ◽  
Health Organization

Malaria continues to be one of the leading causes of death in the world, despite the massive efforts put forth by World Health Organization (WHO) in eradicating it, worldwide. Efficient control and proper treatment of this disease requires early detection and accurate diagnosis due to the large number of cases reported yearly. To achieve this aim, this paper proposes a malaria parasite segmentation approach via cascaded clustering algorithms to automate the malaria diagnosis process. The comparisons among the cascaded clustering algorithms have been made by considering the accuracy, sensitivity and specificity of the segmented malaria images. Based on the qualitative and quantitative findings, the results show that by using the final centres that have been generated by enhanced k-means (EKM) clustering as the initial centres for fuzzy c-means (FCM) clustering, has led to the production of good segmented malaria image. The proposed cascaded EKM and FCM clustering has successfully segmented 100 malaria images of Plasmodium Vivax species with average segmentation accuracy, sensitivity and specificity values of 99.22%, 88.84% and 99.56%, respectively. Therefore, the EKM algorithm has given the best performance compared to k-means (KM) and moving k-means (MKM) algorithms when all the three clustering algorithms are cascaded with FCM algorithm.

scholarly journals FCM Clustering Algorithms for Segmentation of Brain MR Images

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Yogita K. Dubey ◽  
Milind M. Mushrif
Keyword(s):  
Clustering Algorithms ◽  
Intensity Inhomogeneity ◽  
Noise Robustness ◽  
Mr Images ◽  
Tissue Segmentation ◽  
Brain Images ◽  
Fcm Clustering ◽  
Weak Boundaries ◽  
Brain Mr Images ◽  
Mr Brain Images

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-means (FCM) clustering algorithms for the segmentation of brain MR images. The review covers the detailed analysis of FCM based algorithms with intensity inhomogeneity correction and noise robustness. Different methods for the modification of standard fuzzy objective function with updating of membership and cluster centroid are also discussed.

scholarly journals A Hybrid Method for Image Segmentation Based on Artificial Fish Swarm Algorithm and Fuzzyc-Means Clustering

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Li Ma ◽  
Yang Li ◽  
Suohai Fan ◽  
Runzhu Fan
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Clustering Algorithms ◽  
Optimal Solution ◽  
Medical Image Segmentation ◽  
Superior Result ◽  
Artificial Fish Swarm Algorithm ◽  
Artificial Fish Swarm ◽  
Fcm Clustering ◽  
Swarm Algorithm

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 are utilized to find a superior result. Meanwhile, Metropolis criterion and noise reduction mechanism are introduced to AFSA for enhancing the convergence rate and antinoise ability. The artificial grid graph and Magnetic Resonance Imaging (MRI) are used in the experiments, and the experimental results show that the proposed algorithm has stronger antinoise ability and higher precision. A number of evaluation indicators also demonstrate that the effect of HAFSA is more excellent than FCM and suppressed FCM (SFCM).

scholarly journals Cluster Head Selection and Packet Delivery Estimation Based on K-Means and FCM

2019 ◽  
Vol 8 (4) ◽  
pp. 10028-10036
Keyword(s):  
Comparative Study ◽  
Clustering Algorithms ◽  
Cluster Head ◽  
Time Frame ◽  
Cluster Head Selection ◽  
Delivery Time ◽  
Packet Delivery ◽  
Fuzzy C Means ◽  
Fcm Clustering

In this paper comparative study have been presented for the efficient cluster head selection based on k-means and fuzzy c-means (FCM) clustering algorithms. It is observed that the nodes assignment after the clustering is different through k-means and FCM. It is because of the variant initialization mechanism of the k-means and FCM. But the assignment of cluster does not affect the results. It is clearly depicted from the packet delivery time results by our approach. It shows that the k-means and FCM have the capability of CHs selection in the required time frame and it shows the effectiveness in different iterations also. When aggregate packet delivery has been considered the same situation has been observed which depicts the capability of our approach. K-means found to be faster in comparison to FCM.

scholarly journals A Fusion Method of Gabor Wavelet Transform and Unsupervised Clustering Algorithms for Tissue Edge Detection

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Burhan Ergen
Keyword(s):  
Wavelet Transform ◽  
Edge Detection ◽  
Medical Images ◽  
Clustering Algorithms ◽  
Unsupervised Clustering ◽  
Gabor Wavelet ◽  
Detection Methods ◽  
Gabor Wavelet Transform ◽  
Fcm Clustering ◽  
Magnetic Resonance Imaging Mri

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 successful for edge detection, even in noisy cases.

Clustering Algorithm in Possibilistic Exponential Fuzzy C-Mean Segmenting Medical Images

2017 ◽  
Vol 30 ◽  
pp. 12-23 ◽  
Author(s):  
Chiranji Lal Chowdhary ◽  
D.P. Acharjya
Keyword(s):  
Clustering Algorithm ◽  
Medical Images ◽  
Clustering Algorithms ◽  
Probabilistic Constraints ◽  
Fuzzy C Means ◽  
Average Accuracy ◽  
Segmentation Approach ◽  
Fcm Clustering ◽  
Fuzzy Segmentation ◽  
Result Analysis

Different fuzzy segmentation methods were used in medical imaging from last two decades for obtaining better accuracy in various approaches like detecting tumours etc. Well-known fuzzy segmentations like fuzzy c-means (FCM) assign data to every cluster but that is not realistic in few circumstances. Our paper proposes a novel possibilistic exponential fuzzy c-means (PEFCM) clustering algorithm for segmenting medical images. This new clustering algorithm technology can maintain the advantages of a possibilistic fuzzy c-means (PFCM) and exponential fuzzy c-mean (EFCM) clustering algorithms to maximize benefits and reduce noise/outlier influences. In our proposed hybrid possibilistic exponential fuzzy c-mean segmentation approach, exponential FCM intention functions are recalculated and that select data into the clusters. Traditional FCM clustering process cannot handle noise and outliers so we require being added in clusters due to the reasons of common probabilistic constraints which give the total of membership’s degree in every cluster to be 1. We revise possibilistic exponential fuzzy clustering (PEFCM) which hybridize possibilistic method over exponential fuzzy c-mean segmentation and this proposed idea partition the data filters noisy data or detects them as outliers. Our result analysis by PEFCM segmentation attains an average accuracy of 97.4% compared with existing algorithms. It was concluded that the possibilistic exponential fuzzy c-means segmentation algorithm endorsed for additional efficient for accurate detection of breast tumours to assist for the early detection.

Emphasizing the Digital Shift of Hospitality Towards Hyper-Personalization

2022 ◽  
pp. 1-19
Author(s):  
Nuno Gustavo ◽  
Elliot Mbunge ◽  
Miguel Belo ◽  
Stephen Gbenga Fashoto ◽  
João Miguel Pronto ◽  
...  
Keyword(s):  
Execution Time ◽  
Clustering Algorithms ◽  
Fuzzy C Means ◽  
The Past ◽  
Personalized Services ◽  
Fcm Clustering ◽  
Number Of Iterations

This chapter aims to review the tech evolution in hospitality, from services to eServices, that will provide hyper-personalization in the hospitality field. In the past, the services were provided by hotels through diligent staff and supported by standardized and weak technology that was not allowed to provide personalized services by itself. Therefore, the study applied K-means and FCM clustering algorithms to cluster online travelers' reviews from TripAdvisor. The study shows that K-means clustering outperforms fuzzy c-means in this study in terms of accuracy and execution time while fuzzy c-means converge faster than K-means clustering in terms of the number of iterations. K-means achieved 93.4% accuracy, and fuzzy c-means recorded 91.3% accuracy.

scholarly journals Một phương pháp mới nâng cao độ tương phản ảnh màu theo hướng tiếp cận trực tiếp

2017 ◽  
pp. 59
Author(s):  
Nguyễn Văn Quyền ◽  
Nguyễn Tân Ân ◽  
Trần Thái Sơn ◽  
Ngô Hoàng Huy ◽  
Đặng Duy An
Keyword(s):  
Direct Method ◽  
Clustering Algorithms ◽  
Direct Methods ◽  
Color Images ◽  
Indirect Methods ◽  
Image Contrast Enhancement ◽  
Contrast Measure ◽  
Fcm Clustering ◽  
Different Color ◽  
Contrast Measurement

Image contrast enhancement techniques have two mainly methods: indirect method and direct method. While indirect methods only modify the histogram without defining any specific contrast measure, the direct methods establish a criterion of contrast measurement and enhance the image by improving the contrast measure. Among many direct methods, only the studies by Cheng and Xu modified the contrast at each point of grayscale image using a contrast measure [6, 7]. In this paper we propose a new method for enhancing the contrast of color images based on the direct method. The experimental results demonstrate that the combination of our proposed method with Fuzzy C_Mean (FCM) clustering algorithms performs well on different color images.

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