A Novel Segmentation Method for Brain MRI Using a Block-Based Integrated Fuzzy C-Means Clustering Algorithm

2020 ◽  
Vol 10 (3) ◽  
pp. 579-585
Author(s):  
Hui Zhang ◽  
Hongjie Zhang
Keyword(s):  
Clustering Algorithm ◽  
Brain Diseases ◽  
Cluster Center ◽  
Large Sample Size ◽  
Large Sample ◽  
Fcm Algorithm ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Final Cluster ◽  
Block Based

Accurate segmentation of brain tissue has important guiding significance and practical application value for the diagnosis of brain diseases. Brain magnetic resonance imaging (MRI) has the characteristics of high dimensionality and large sample size. Such datasets create considerable computational complexity in image processing. To efficiently process large sample data, this article integrates the proposed block clustering strategy with the classic fuzzy C-means clustering (FCM) algorithm and proposes a block-based integrated FCM clustering algorithm (BI-FCM). The algorithm first performs block processing on each image and then clusters each subimage using the FCM algorithm. The cluster centers for all subimages are again clustered using FCM to obtain the final cluster center. Finally, the distance from each pixel to the final cluster center is obtained, and the corresponding division is performed according to the distance. The dataset used in this experiment is the Simulated Brain Database (SBD). The results show that the BI-FCM algorithm addresses the large sample processing problem well, and the theory is simple and effective.

Improved Fuzzy FCM-LI Algorithm

2013 ◽  
Vol 765-767 ◽  
pp. 670-673
Author(s):  
Li Bo Hou
Keyword(s):  
Real Time ◽  
Clustering Algorithm ◽  
Feature Analysis ◽  
Cluster Center ◽  
High Dimensional ◽  
Fuzzy C Means ◽  
Sample Data ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering ◽  
Np Hard Problem

Fuzzy C-means (FCM) clustering algorithm is one of the widely applied algorithms in non-supervision of pattern recognition. However, FCM algorithm in the iterative process requires a lot of calculations, especially when feature vectors has high-dimensional, Use clustering algorithm to sub-heap, not only inefficient, but also may lead to "the curse of dimensionality." For the problem, This paper analyzes the fuzzy C-means clustering algorithm in high dimensional feature of the process, the problem of cluster center is an np-hard problem, In order to improve the effectiveness and Real-time of fuzzy C-means clustering algorithm in high dimensional feature analysis, Combination of landmark isometric (L-ISOMAP) algorithm, Proposed improved algorithm FCM-LI. Preliminary analysis of the samples, Use clustering results and the correlation of sample data, using landmark isometric (L-ISOMAP) algorithm to reduce the dimension, further analysis on the basis, obtained the final results. Finally, experimental results show that the effectiveness and Real-time of FCM-LI algorithm in high dimensional feature analysis.

An Energy-Efficient Clustering Algorithm Using Fuzzy C-Means and Genetic Fuzzy System for Wireless Sensor Network

2016 ◽  
Vol 26 (01) ◽  
pp. 1750004 ◽  
Author(s):  
Ayub Shokrollahi ◽  
Babak Mazloom-Nezhad Maybodi
Keyword(s):  
Energy Consumption ◽  
Fuzzy System ◽  
Energy Efficient ◽  
Clustering Algorithm ◽  
Wireless Sensor ◽  
Cluster Center ◽  
Fcm Algorithm ◽  
Fuzzy C Means ◽  
Genetic Fuzzy System ◽  
Energy Efficient Clustering

The energy efficiency in wireless sensor networks (WSNs) is a fundamental challenge. Cluster-based routing is an energy saving method in this type of networks. This paper presents an energy-efficient clustering algorithm based on fuzzy c-means algorithm and genetic fuzzy system (ECAFG). By using FCM algorithm, the clusters are formed, and then cluster heads (CHs) are selected utilizing GFS. The formed clusters will be remaining static but CHs are selected at the beginning of each round. FCM algorithm forms balanced clusters and distributes the consumed energy among them. Using static clusters also reduces the data overhead and consequently the energy consumption. In GFS, nodes energy, the distance from nodes to the base station and the distance from each node to its corresponding cluster center are considered as determining factors in CHs selection. Then, genetic algorithm is also used to obtain fuzzy if–then rules of GFS. Consequently, the system performance is improved and appropriate CHs can be selected, hence energy dissipation is reduced. The simulation results show that ECAFG, compared with the existing methods, significantly reduces the energy consumption of the sensor nodes, and prolongs the network lifetime.

AN UNSUPERVISED KERNEL BASED FUZZY C-MEANS CLUSTERING ALGORITHM WITH KERNEL NORMALISATION

2004 ◽  
Vol 04 (04) ◽  
pp. 355-373 ◽  
Author(s):  
SHANG-MING ZHOU ◽  
JOHN Q. GAN
Keyword(s):  
Data Clustering ◽  
Clustering Algorithm ◽  
Experimental Results ◽  
New Method ◽  
Fcm Algorithm ◽  
Input Space ◽  
Fuzzy C Means ◽  
Benchmark Datasets ◽  
Fuzzy C Means Clustering ◽  
Mercer Kernel

In this paper, a novel procedure for normalising Mercer kernel is suggested firstly. Then, the normalised Mercer kernel techniques are applied to the fuzzy c-means (FCM) algorithm, which leads to a normalised kernel based FCM (NKFCM) clustering algorithm. In the NKFCM algorithm, implicit assumptions about the shapes of clusters in the FCM algorithm is removed so that the new algorithm possesses strong adaptability to cluster structures within data samples. Moreover, a new method for calculating the prototypes of clusters in input space is also proposed, which is essential for data clustering applications. Experimental results on several benchmark datasets have demonstrated the promising performance of the NKFCM algorithm in different scenarios.

Application of artificial fish swarm optimization semi-supervised kernel fuzzy clustering algorithm in network intrusion

2020 ◽  
Vol 39 (2) ◽  
pp. 1619-1626
Author(s):  
Yongsheng Zong ◽  
Guoyan Huang
Keyword(s):  
Intrusion Detection ◽  
Detection Rate ◽  
Clustering Algorithm ◽  
Probabilistic Constraints ◽  
Cluster Center ◽  
Swarm Optimization ◽  
Fuzzy C Means ◽  
Network Intrusion ◽  
Fuzzy C Means Clustering ◽  
Artificial Fish Swarm

For the unsupervised learning based clustering algorithm, the intrusion detection rate is low, and the training sample based on supervised learning clustering algorithm is insufficient. A semi-supervised kernel fuzzy C-means clustering algorithm based on artificial fish swarm optimization (AFSA-KFCM) is proposed. Firstly, the kernel function is used to change the distance function in the traditional semi-supervised fuzzy C-means clustering algorithm to define a new objective function, thus improving the probabilistic constraints of the fuzzy C-means algorithm. Then, the artificial fish swarm algorithm with strong global optimization ability is used to improve the KFCM sensitivity to the initial cluster center and easy to fall into the local extremum, thus improving the convergence speed and improving the classification effect. The test results in the Wine and IRIS public datasets show that the AFSA-KFCM clustering algorithm is superior to the traditional algorithm in clustering accuracy and time efficiency. At the same time, the experimental results in KDDCUP99 experimental data show that the algorithm can obtain the ideal detection rate and false detection rate in intrusion detection.

An Improved FCM Algorithm Based on Subtractive Clustering for Power Load Classification

2014 ◽  
Vol 986-987 ◽  
pp. 206-210 ◽  
Author(s):  
Rui Dong ◽  
Min Xiang Huang
Keyword(s):  
Experimental Analysis ◽  
Global Search ◽  
Cluster Center ◽  
Subtractive Clustering ◽  
Local Optima ◽  
Fcm Algorithm ◽  
Fuzzy C Means ◽  
Power Load ◽  
Fuzzy C Means Clustering ◽  
Random Initialization

FCM is used in many power load classification currently, but it also has some shortcomings. This paper give an algorithm based on Subtractive Clustering and improved Fuzzy C-means Clustering (SUB-FCM) to solve this problem. This algorithm use subtractive clustering to initialize the cluster center matrix, solve the random initialization of FCM, and improve the global search ability, avoid falling into local optima. Experimental analysis found this algorithm also could accelerate the convergence speed, and has better clustering results. It can be applied to power load classification effectively.

Fuzzy C-Means Clustering Algorithm Based on Coefficient of Variation

2014 ◽  
Vol 998-999 ◽  
pp. 873-877
Author(s):  
Zhen Bo Wang ◽  
Bao Zhi Qiu
Keyword(s):  
Coefficient Of Variation ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Real Data ◽  
Cluster Center ◽  
Data Set ◽  
Fuzzy C Means ◽  
Initial Cluster ◽  
Fuzzy C Means Clustering ◽  
The Impact

To reduce the impact of irrelevant attributes on clustering results, and improve the importance of relevant attributes to clustering, this paper proposes fuzzy C-means clustering algorithm based on coefficient of variation (CV-FCM). In the algorithm, coefficient of variation is used to weigh attributes so as to assign different weights to each attribute in the data set, and the magnitude of weight is used to express the importance of different attributes to clusters. In addition, for the characteristic of fuzzy C-means clustering algorithm that it is susceptible to initial cluster center value, the method for the selection of initial cluster center based on maximum distance is introduced on the basis of weighted coefficient of variation. The result of the experiment based on real data sets shows that this algorithm can select cluster center effectively, with the clustering result superior to general fuzzy C-means clustering algorithms.

scholarly journals Differential privacy fuzzy C-means clustering algorithm based on gaussian kernel function

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248737
Author(s):  
Yaling Zhang ◽  
Jin Han
Keyword(s):  
Privacy Protection ◽  
Clustering Algorithm ◽  
Differential Privacy ◽  
Gaussian Kernel ◽  
Cluster Center ◽  
User Privacy ◽  
Distance Method ◽  
Fuzzy C Means ◽  
Center Point ◽  
Fuzzy C Means Clustering

Fuzzy C-means clustering algorithm is one of the typical clustering algorithms in data mining applications. However, due to the sensitive information in the dataset, there is a risk of user privacy being leaked during the clustering process. The fuzzy C-means clustering of differential privacy protection can protect the user’s individual privacy while mining data rules, however, the decline in availability caused by data disturbances is a common problem of these algorithms. Aiming at the problem that the algorithm accuracy is reduced by randomly initializing the membership matrix of fuzzy C-means, in this paper, the maximum distance method is firstly used to determine the initial center point. Then, the gaussian value of the cluster center point is used to calculate the privacy budget allocation ratio. Additionally, Laplace noise is added to complete differential privacy protection. The experimental results demonstrate that the clustering accuracy and effectiveness of the proposed algorithm are higher than baselines under the same privacy protection intensity.

Embedding Global Optimization and Kernelization into Fuzzy C-Means Clustering for Consonant/Vowel Segmentation

2013 ◽  
Vol 419 ◽  
pp. 814-819
Author(s):  
Xian Zang ◽  
Kil To Chong
Keyword(s):  
Global Optimization ◽  
Clustering Algorithm ◽  
Optimal Solution ◽  
Cluster Center ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Simulation Results

This paper proposes a novel clustering algorithm named global kernel fuzzy-c means (GK-FCM) to segment the speech into small non-overlapping blocks for consonant/vowel segmentation. This algorithm is realized by embedding global optimization and kernelization into the classical fuzzy c-means clustering algorithm. It proceeds in an incremental way attempting to optimally add new cluster center at each stage through the kernel-based fuzzy c-means. By solving all the intermediate problems, the final near-optimal solution is determined in a deterministic way. This algorithm overcomes the well-known shortcomings of fuzzy c-means and improves the clustering accuracy. Simulation results demonstrate the effectiveness of the proposed method in consonant/vowel segmentation.

scholarly journals Research on Classification of Daily Load Curve of Distribution Network Based on Improved SSA-FCM

2021 ◽  
Vol 2121 (1) ◽  
pp. 012011
Author(s):  
Haoran Shi ◽  
Rong Cao ◽  
Wenbo Hao ◽  
Mingyu Xu ◽  
Heng Hu ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Search Algorithm ◽  
Distribution Network ◽  
Optimal Solution ◽  
Cluster Center ◽  
Load Curve ◽  
Fcm Algorithm ◽  
Fuzzy C Means ◽  
Three Phase ◽  
Daily Load

Abstract In the analysis of three-phase unbalance in distribution network, the accuracy of daily load curve classification results determines the size of three-phase unbalance. Aiming at the shortcomings of Fuzzy C-Means (FCM), a fuzzy C-Means clustering algorithm (SSA-FCM) optimized based on Sparrow Search Algorithm (SSA) is proposed. The cluster validity evaluation index is introduced to get the optimal quantity of clusters, and the SSA is used to search for the initial cluster center, which solves the problem that the FCM algorithm relies on the initial value and is easy to converge to local optimal solution. The simulation results show that, compared with the FCM algorithm, the load curves classified into the same category by SSA-FCM are closer together.

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