Semi-supervised Fuzzy C-means Clustering Research Articles

Semi-supervised fuzzy C means based on membership integration mechanism and its application in brain infarction lesion segmentation in DWI images

In this paper, a novel semi-supervised fuzzy clustering algorithm, MFM-SFCM, based on a membership fusion mechanism is proposed for Diffusion-weighted imaging (DWI) brain infarction lesion segmentation. The proposed MFM-SFCM algorithm addresses the issue of weakened constraints and insufficient influence of labeled samples on the clustering process that arises in the semi-supervised fuzzy C-means clustering (SFCM) when emphasizing supervised information. By using a new membership fusion mechanism, MFM-SFCM eliminates this issue, greatly improving the accuracy of clustering results and accelerating convergence speed. This allows fuzzy clustering to achieve good results in the segmentation of DWI brain infarction lesions using a small amount of labeled information. The effectiveness of the MFM-SFCM algorithm is demonstrated through experiments conducted on a real-world dataset of DWI brain images.

Research on lithium battery sorting method based on image adaptive recognition of feature points

When sorting batteries in factories, fixed feature points in a discharge voltage platform are usually used as a sorting basis with expert experience, causing poor accuracy in battery sorting. This study proposes a method based on feature point image adaptive recognition (FPIAR) and combined a semi-supervised fuzzy C-means (SFCM) clustering algorithm to achieve battery sorting. Accordingly, FPIAR obtained the multi-dimensional feature coordinate dataset by adaptively identifying the discharge voltage curve of each battery. The centre point of the accumulation of coordinates in the dataset was found by mean-shift algorithm and was used as the feature point of battery sorting voltage. In addition, the SFCM clustering algorithm was used to process the feature point of the sorting voltage to sort the batteries into groups. The simulation results and tests indicated that the SFCM clustering algorithm based on FPIAR processing can improve the accuracy of battery sorting.

Robust Semisupervised Kernelized Fuzzy Local Information C-Means Clustering for Image Segmentation

To improve the effectiveness and robustness of the existing semisupervised fuzzy clustering for segmenting image corrupted by noise, a kernel space semisupervised fuzzy C-means clustering segmentation algorithm combining utilizing neighborhood spatial gray information with fuzzy membership information is proposed in this paper. The mean intensity information of neighborhood window is embedded into the objective function of the existing semisupervised fuzzy C-means clustering, and the Lagrange multiplier method is used to obtain its iterative expression corresponding to the iterative solution of the optimization problem. Meanwhile, the local Gaussian kernel function is used to map the pixel samples from the Euclidean space to the high-dimensional feature space so that the cluster adaptability to different types of image segmentation is enhanced. Experiment results performed on different types of noisy images indicate that the proposed segmentation algorithm can achieve better segmentation performance than the existing typical robust fuzzy clustering algorithms and significantly enhance the antinoise performance.

Semi-Supervised Fuzzy C-Means Clustering Optimized by Simulated Annealing and Genetic Algorithm for Fault Diagnosis of Bearings

As a popular clustering algorithms, fuzzy c-means (FCM) algorithm has been used in various fields, including fault diagnosis, machine learning. To overcome the sensitivity to outliers problem and the local minimum problem of the fuzzy c-means new algorithm is proposed based on the simulated annealing (SA) algorithm and the genetic algorithm (GA). The combined algorithm utilizes the simulated annealing algorithm due to its local search abilities. Thereby, problems associated with the genetic algorithm, such as its tendency to prematurely select optimal values, can be overcome, and genetic algorithm can be applied in fuzzy clustering analysis. Moreover, the new algorithm can solve other problems associated with the fuzzy clustering algorithm, which include initial clustering center value sensitivity and convergence to a local minimum. Furthermore, the simulation results can be used as classification criteria for identifying several types of bearing faults. Compare with the dimensionless indexes, it shows that the mutual dimensionless indexes are more suitable for clustering algorithms. Finally, the experimental results show that the method adopted in this paper can improve the accuracy of clustering and accurately classify the bearing faults of rotating machinery.

МЕТОД ЗАБЕЗПЕЧЕННЯ РЕЗИЛЬЄНТНОСТІ КОМП’ЮТЕРНИХ СИСТЕМ В УМОВАХ КІБЕРЗАГРОЗ НА ОСНОВІ САМОАДАПТИВНОСТІ

The dynamic expansion of cyber threats poses an urgent need for the development of new methods, methods, and systems for their detection. The subject of the study is the process of ensuring the resilience of computer systems in the presence of cyber threats. The goal is to develop a self-adaptive method for computer systems resilience in the presence of cyberattacks. Results. The article presents a self-adaptive system to ensure the resilience of corporate networks in the presence of botnets’ cyberattacks. Resilience is provided by adaptive network reconfiguration. It is carried out using security scenarios selected based on a cluster analysis of the collected network features inherent cyberattacks. To select the necessary security scenarios, the proposed method uses fuzzy semi-supervised c-means clustering. To detect host-type cyberattacks, information about the hosts’ network activity and reports of host antiviruses are collected. To detect the network type attacks, the monitoring of network activity is carried out, which may indicate the appearance of a cyberattack. According to gathered in the network information concerning possible attacks performed by botnet the measures for the resilient functioning of the network are assumed. To choose the needed scenario for network reconfiguration, the clustering is performed. The result of the clustering is the scenario with the list of the requirement for the reconfiguration of the network parameters, which will assure the network’s resilience in the situation of the botnet’s attacks. As the mean of the security scenario choice, the semi-supervised fuzzy c-means clustering was used. The clustering is performed based on labeled training data. The objects of the clustering are the feature vectors, obtained from a payload of the inbound and outbound traffic and reports of the antiviral tool about possible hosts’ infection. The result of clustering is a degree of membership of the feature vectors to one of the clusters. The membership of feature vector to cluster gives an answer to question what scenario of the network reconfiguration is to be applied in the situation of the botnet’s attack. The system contains the clusters that indicate the normal behavior of the network. The purpose of the method is to select security scenarios following cyberattacks carried out by botnets to mitigate the consequences of attacks and ensure a network functioning resilience. Conclusions. The self-adaptive method for computer systems resilience in the presence of cyberattacks has been developed. Based on the proposed method, a self-adaptive attack detection, and mitigation system has been developed. It demonstrates the ability to ensure the resilient functioning of the network in the presence of botnet cyberattacks at 70 %.

Fault Diagnosis for Rolling Bearing Based on Semi-Supervised Clustering and Support Vector Data Description with Adaptive Parameter Optimization and Improved Decision Strategy

Rolling bearing is of great importance in modern industrial products, the failure of which may result in accidents and economic losses. Therefore, fault diagnosis of rolling bearing is significant and necessary and can enhance the reliability and efficiency of mechanical systems. Therefore, a novel fault diagnosis method for rolling bearing based on semi-supervised clustering and support vector data description (SVDD) with adaptive parameter optimization and improved decision strategy is proposed in this study. First, variational mode decomposition (VMD) was applied to decompose the vibration signals into sets of intrinsic mode functions (IMFs), where the decomposing mode number K was determined by the central frequency observation method. Next, fuzzy entropy (FuzzyEn) values of all IMFs were calculated to construct the feature vectors of different types of faults. Later, training samples were clustered with semi-supervised fuzzy C-means clustering (SSFCM) for fully exploiting the information inside samples, whereupon a small number of labeled samples were able to provide sufficient data distribution information for subsequent SVDD algorithms and improve its recognition ability. Afterwards, SVDD with improved decision strategy (ID-SVDD) that combined with k-nearest neighbor was proposed to establish diagnostic model. Simultaneously, the optimal parameters C and σ for ID-SVDD were searched by the newly proposed sine cosine algorithm improved with adaptive updating strategy (ASCA). Finally, the proposed diagnosis method was applied for engineering application as well as contrastive analysis. The obtained results reveal that the proposed method exhibits the best performance in all evaluation metrics and has advantages over other comparison methods in both precision and stability.

Remote sensing classification method of vegetation dynamics based on time series Landsat image: a case of opencast mining area in China

Time series remote sensing image is an important resource for dynamic monitoring of resources and environment, and its abundant time spectrum information can be used to characterize the dynamic change of vegetation coverage. This paper proposes a comprehensive clustering and pixel classification method for extracting the vegetation dynamics based on time series Landsat normalized difference vegetation index (NDVI). This method uses the time-division algorithm for fitting time-series NDVI firstly. And the Markov random field optimized (MRF) semi-supervised dynamic time warping (DTW) kernel fuzzy c-means clustering was constructed. Then the MRF-optimized semi-supervised DTW-kernel fuzzy c-means clustering was combined with the 1-nearest neighbor (1NN) DTW pixel classification to realize the extraction of vegetation dynamics. Shengli Opencast Coal Mine in The Xilin Gol Grassland was taken as the study area to analyze the applicability of the different classification methods. The results showed the fusion algorithm of the MRF-Semi-GDTW-FCM and 1NN-DTW generates accurate classification results with the overall accuracy of 93.8806% and Kappa coefficient of 0.9267, which were 1.7219, 0.0182, and 20.4080% and 0.2916 higher than the clustering and pixel classification, respectively. Experiments proof that the method proposed in this paper is not only simple but also accurate and effective.

Novel Approach to Unsupervised Change Detection Based on a Robust Semi-Supervised FCM Clustering Algorithm

This study presents a novel approach for unsupervised change detection in multitemporal remotely sensed images. This method addresses the problem of the analysis of the difference image by proposing a novel and robust semi-supervised fuzzy C-means (RSFCM) clustering algorithm. The advantage of the RSFCM is to further introduce the pseudolabels from the difference image compared with the existing change detection methods; these methods, mainly use difference intensity levels and spatial context. First, the patterns with a high probability of belonging to the changed or unchanged class are identified by selectively thresholding the difference image histogram. Second, the pseudolabels of these nearly certain pixel-patterns are jointly exploited with the intensity levels and spatial information in the properly defined RSFCM classifier in order to discriminate the changed pixels from the unchanged pixels. Specifically, labeling knowledge is used to guide the RSFCM clustering process to enhance the change information and obtain a more accurate membership; information on spatial context helps to lower the effect of noise and outliers by modifying the membership. RSFCM can detect more changes and provide noise immunity by the synergistic exploitation of pseudolabels and spatial context. The two main contributions of this study are as follows: (1) it proposes the idea of combining the three information types from the difference image, namely, (a) intensity levels, (b) labels, and (c) spatial context; and (2) it develops the novel RSFCM algorithm for image segmentation and forms the proposed change detection framework. The proposed method is effective and efficient for change detection as confirmed by six experimental results of this study.

On semi-supervised fuzzy c-means clustering for data with clusterwise tolerance by opposite criteria

This paper presents a new semi-supervised fuzzy c-means clustering for data with clusterwise tolerance by opposite criteria. In semi-supervised clustering, pairwise constraints, that is, must-link and cannot-link, are frequently used in order to improve clustering performances. From the viewpoint of handling pairwise constraints, a new semi-supervised fuzzy c-means clustering is proposed by introducing clusterwise tolerance-based pairwise constraints. First, a concept of clusterwise tolerance-based pairwise constraints is introduced. Second, the optimization problems of the proposed method are formulated. Especially, must-link and cannot-link are handled by opposite criteria in our proposed method. Third, a new clustering algorithm is constructed based on the above discussions. Finally, the effectiveness of the proposed algorithm is verified through numerical examples.

Engine Wear Fault Diagnosis Based on Improved Semi-supervised Fuzzy C-means Clustering

A improved semi-supervised fuzzy c-means clustering algorithm(ISS-FCM) is proposed to diagnose engine wear faults with small oil samples.An optimized objective function,which is defined through introducing average distance measure between unlabeled samples and training samples with weighting values,is used to conduct the clustering process.To avoid local extrema originating from initialing partition matrix randomly,the training samples are utilized in partition matrix initialing work.By reason that engine wear condition can not be effectively characterized by original oil data with unobvious cluster trendency,Autoregression(AR) model is used to abstract the residual variance features from oil data.The atomic emission spectrometric oil data of engine bench test are analyzed with the proposed method.The cylinder scoring and bushing ablating faults are diagnosed successfully.Experimental results demonstrate the validity of the presented method in the field of engine wear fault diagnosis.

Monitoring brain tumor response to therapy using MRI segmentation

The performance evaluation of a semi-supervised fuzzy c-means (SFCM) clustering method for monitoring brain tumor volume changes during the course of routine clinical radiation-therapeutic and chemo-therapeutic regimens is presented. The tumor volume determined using the SFCM method was compared with the volume estimates obtained using three other methods: (a) a k nearest neighbor (kNN) classifier, b) a grey level thresholding and seed growing (ISG-SG) method and c) a manual pixel labeling (GT) method for ground truth estimation. The SFCM and kNN methods are applied to the multispectral, contrast enhanced T 1, proton density, and T 2 weighted, magnetic resonance images (MRI) whereas the ISG-SG and GT methods are applied only to the contrast enhanced T 1 weighted image. Estimations of tumor volume were made on eight patient cases with follow-up MRI scans performed over a 32 week interval during treatment. The tumor cases studied include one meningioma, two brain metastases and five gliomas. Comparisons with manually labeled ground truth estimations showed that there is a limited agreement between the segmentation methods for absolute tumor volume measurements when using images of patients after treatment. The average intraobserver reproducibility for the SFCM, kNN and ISG-SG methods was found to be 5.8%, 6.6% and 8.9%, respectively. The average of the interobserver reproducibility of these methods was found to be 5.5%, 6.5% and 11.4%, respectively. For the measurement of relative change of tumor volume as required for the response assessment, the multi-spectral methods kNN and SFCM are therefore preferred over the seedgrowing method.