Kernel Fuzzy C-means Clustering Research Articles

Reconstruction-Aware Kernelized Fuzzy Clustering Framework Incorporating Local Information for Image Segmentation

Kernelized fuzzy C-means clustering with weighted local information is an extensively applied robust segmentation algorithm for noisy image. However, it is difficult to effectively solve the problem of segmenting image polluted by strong noise. To address this issue, a reconstruction-aware kernel fuzzy C-mean clustering with rich local information is proposed in this paper. Firstly, the optimization modeling of guided bilateral filtering is given for noisy image; Secondly, this filtering model is embedded into kernelized fuzzy C-means clustering with local information, and a novel reconstruction-filtering information driven fuzzy clustering model for noise-corrupted image segmentation is presented; Finally, a tri-level alternative and iterative algorithm is derived from optimizing model using optimization theory and its convergence is strictly analyzed. Many Experimental results on noisy synthetic images and actual images indicate that compared with the latest advanced fuzzy clustering-related algorithms, the algorithm presented in this paper has better segmentation performance and stronger robustness to noise, and its PSNR and ACC values increase by about 0.16–3.28 and 0.01–0.08 respectively.

An intelligent interval forecasting system based on fuzzy time series and error distribution characteristics for air quality index

Due to the emergency environment pollution problems, it is imperative to understand the air quality and take effective measures for environmental governance. As a representative measure, the air quality index (AQI) is a single conceptual index value simplified by the concentrations of several routinely monitored air pollutants according to the proportion of various components in the air. With the gradual enhancement of awareness of environmental protection, air quality index forecasting is a key point of environment management. However, most of the traditional forecasting methods ignore the fuzziness of original data itself and the uncertainty of forecasting results which causes the unsatisfactory results. Thus, an innovative forecasting system combining data preprocessing technique, kernel fuzzy c-means (KFCM) clustering algorithm and fuzzy time series is successfully developed for air quality index forecasting. Concretely, the fuzzy time series that handle the fuzzy set is used for the main forecasting process. Then the complete ensemble empirical mode decomposition and KFCM are respectively developed for data denoising and interval partition. Furthermore, the interval forecasting method based on error distribution is developed to measure the forecasting uncertainty. Finally, the experimental simulation and evaluation system verify the great performance of proposed forecasting system and the promising applicability in a practical environment early warning system.

Multi‐model predictive control of SCR flue gas denitrification system in coal‐fired power plant based on kernel fuzzy c‐means clustering and integrated model

AbstractA multi‐model predictive control strategy based on kernel fuzzy c‐means (KFCM) clustering and integrated model is proposed for the complex problem of rapid and accurate control of ammonia injection in selective catalytic reduction (SCR) denitrification systems of coal‐fired power plants under a wide range of variable load conditions. First, the SCR data samples are clustered using the KFCM clustering algorithm, and the number of clusters is determined by introducing the Xie‐Beni index. Second, the prediction model of the SCR denitrification system is established by an integrated modelling approach, and the sub‐learners of the integrated model are the genetic algorithm optimized back propagation (GA‐BP) neural network model and the least squares support vector machine (LSSVM) model. Third, a multi‐model prediction controller based on the particle swarm optimization (PSO) algorithm and the integrated model is designed and developed. To ensure the stability of the system, a model‐switching strategy based on the minimum Euclidean distance is proposed. Finally, simulation verification and industrial field application verification are fulfilled by comparing with proportion integral differential (PID) control and single model predictive control (MPC). The results show that the multi‐model predictive control method proposed in this paper can obtain higher control accuracy and better control stability and meet the control requirements for the long‐term operation of the SCR denitrification system.

Retinal Blood-Vessel Extraction Using Weighted Kernel Fuzzy C-Means Clustering and Dilation-Based Functions.

Automated blood-vessel extraction is essential in diagnosing Diabetic Retinopathy (DR) and other eye-related diseases. However, the traditional methods for extracting blood vessels tend to provide low accuracy when dealing with difficult situations, such as extracting both micro and large blood vessels simultaneously with low-intensity images and blood vessels with DR. This paper proposes a complete preprocessing method to enhance original retinal images before transferring the enhanced images to a novel blood-vessel extraction method by a combined three extraction stages. The first stage focuses on the fast extraction of retinal blood vessels using Weighted Kernel Fuzzy C-Means (WKFCM) Clustering to draw the vessel feature from the retinal background. The second stage focuses on the accuracy of full-size images to achieve regional vessel feature recognition of large and micro blood vessels and to minimize false extraction. This stage implements the mathematical dilation operator from a trained model called Dilation-Based Function (DBF). Finally, an optimal parameter threshold is empirically determined in the third stage to remove non-vessel features in the binary image and improve the overall vessel extraction results. According to evaluations of the method via the datasets DRIVE, STARE, and DiaretDB0, the proposed WKFCM-DBF method achieved sensitivities, specificities, and accuracy performances of 98.12%, 98.20%, and 98.16%, 98.42%, 98.80%, and 98.51%, and 98.89%, 98.10%, and 98.09%, respectively.

Cloud-based intrusion detection using kernel fuzzy clustering and optimal type-2 fuzzy neural network

Nowadays, digital data is an important asset for every organization. With the advent of cloud computing, cloud service providers (CSPs) offer the required infrastructure to end-users for storage and provide flexibility in accessing data. Since the users access the data from the cloud through the Internet, the data stored in the cloud are exposed to various intrusions. Intrusion detection is considered to be a significant issue in the cloud. The existing techniques are capable to detect well-known attacks but fall short in detecting low frequent attacks. To address this issue, we propose a novel intrusion detection system (IDS) in the cloud using a combination of kernel fuzzy c-means clustering (KFCM) and an optimal type-2 fuzzy neural network (OT2FNN). To achieve this, we optimally select the parameters of T2FNN using the lion optimization algorithm (LOA) for weight optimization. The proposed IDS detects the intrusion and allow only normal data to be stored in the cloud. Simulation results on the NSL-KDD dataset show that the proposed IDS system gives better results than the existing IDS systems in terms of precision, recall, and F-measure.

Eigenvalue-based cooperative spectrum sensing using kernel fuzzy c-means clustering

In this paper, novel techniques of eigenvalue-based cooperative spectrum sensing (CSS) using Kernel fuzzy c-means (KFCM) clustering are proposed. Test vectors derived from measured eigenvalues are categorized into channel available and unavailable class by performing clustering in two/three dimensional space. This is in contrast to existing eigenvalue-based spectrum sensing techniques, where sensing decision is made on the basis of a single test statistic in one dimension. Though multiple eigenvalues are used in those techniques, finally a single test statistic is computed and used for spectrum sensing. It is shown that proposed CSS using KFCM clustering in multidimensional space provides improvement in detection performance. Three different spectrum sensing techniques, utilizing different combinations of eigenvalues of the signal covariance matrix, are proposed and studied. Proposed algorithm is then compared to some of the recent techniques for spectrum sensing in literature. Extensive simulation results are given which highlight improvement in detection performance offered by the proposed algorithm.

Kernel fuzzy C- means clustering with teaching learning based optimization algorithm (TLBO-KFCM)

In the field of data analysis clustering is considered to be a major tool. Application of clustering in various field of science, has led to advancement in clustering algorithm. Traditional clustering algorithm have lot of defects, while these defects have been addressed but no clustering algorithm can be considered as superior. A new approach based on Kernel Fuzzy C-means clustering using teaching learning-based optimization algorithm (TLBO-KFCM) is proposed in this paper. Kernel function used in this algorithm improves separation and makes clustering more apprehensive. Teaching learning-based optimization algorithm discussed in the paper helps to improve clustering compactness. Simulation using five data sets are performed and the results are compared with two other optimization algorithms (genetic algorithm GA and particle swam optimization PSO). Results show that the proposed clustering algorithm has better performance. Another simulation on same set of data is also performed, and clustering results of TLBO-KFCM are compared with teaching learning-based optimization algorithm with Fuzzy C- Means Clustering (TLBO-FCM).

An Active Contour Method Based on Regularized Kernel Fuzzy C-Means Clustering

This research presents hybrid level set evolution for complex and inhomogeneous image segmentation. Firstly, we develop an adaptive force with level set evolution, which is driven by region information. Adaptive force is produced by consolidating local and global force terms in an altered fashion. Besides, to avoid local fitting terms being stuck into a local minimum, we use the swap function to interchange the fitting terms so that fitting values inside the object are always higher. Later for the elimination of the costly contour initialization that existed in previous level set based evolutions, we integrate kernel based fuzzy c-means clustering and intensity-based thresholding framework with the proposed framework to automate the proposed strategy. Finally, for the level set function regularization and the for the elimination of its re- initialization we have used the Gaussian function in the level set evolution. We demonstrate the results on some complex images to show the strong and exact segmentation results that are conceivable with this new class of adaptive active contour model. We have additionally performed statistical analysis on real images and BRATS dataset using Dice index, accuracy, sensitivity, specificity and Jaccard index metrics. Results show that the proposed method gets high Dice index, accuracy, sensitivity, specificity and Jaccard index values compared to the previous state of art methods.

Moving Object Segmentation Using Level Set Algorithm with GWO-KFCM Clustering

Video analytics is widely used to automatically analyze the videos to extract the required information and detect the various events object identification and traffic analysis. The segmentation of the image is referring to extracting the required region from an image. The major objective of the segmentation process is to cluster the images without being affected by the noises. The detection of the moving objects is a challenging task in video analysis due to the dynamic background of the video. The major drawback of the existing Kernel Fuzzy C-Means (KFCM) clustering is initialization of random centroids which increases the execution time to identify the segmented portions. In this research, the Grey Wolf Optimization (GWO) algorithm used to initialize the centroids of required clusters in KFCM and Level Set (LS) Algorithm is used to segment the objects in video sequence. The proposed KFCM-GWO-LS is implemented for moving and static object detection in the videos obtained from SBM-RGBD dataset. For object detection, determining central clusters are important which is performed by using KFCM. GWO helps in finding the best centroids clusters by matching with KFCM. The centroids clusters are segmented by using LS algorithm which undergoes over segmentation problem that is overcome by GWO. As all the three techniques are dependent on one another hybrid of all these techniques obtains better results. The proposed KFCM-GWO-LS is evaluated in terms of Recall, Specificity, Precision and F-measure and the experimental results showed that the proposed method improved the system performance from 0.3 % to 14.35 % compared to existing methods Multi-Sensor Scheduler Algorithm and Statistical Inference Theory model.

Level Set Segmentation of Oil Spills from Earth Observatory Images Via Spatial KFCM Clustering

In this paper, we present a novel technique called spatial kernel fuzzy clustering with adaptive level set approach for Oil spill image segmentation. The proposed method is diversified into two stages; in the first stage the input is pre-processing by Spatial Kernel Fuzzy C-Means clustering (KFCM) to improve the clustering efficiency and less sensitive to noise. In the second stage, it necessary to use the level set method to refine the previous stage segmentation results. The performance of the level set segmentation is subjected to proper initialization and optimal formation of directing parameters. The controlling parameters of level set evolution are also projected after the results of kernel fuzzy clustering. The proposed method, spatial kernel fuzzy adaptive level set algorithm is enhanced the local minima problem. Such developments enable level set handling and more strong segmentation. The results confirm its effectiveness for oil spill images over the conventional CV model i.e number of iterations, Computational time and PSNR

Top-Down Method Used for Pancreas Segmentation

Image segmentation is actively an imperative title role in image analysis. Image segmentation is advantageous in many applications like traffic detection, surface crack identification, medical image analysis, face recognition, crop disease detection. Two Approaches are used for automatic pancreas segmentation. Top-Down and Bottom-Up approach used for CT image segmentation. In Top Down approach, Grey Level Co-occurrence Matrix, Simple Linear Iterative Clustering, Scale-Invariant Feature transform, Novel Modified Kernel fuzzy c-means clustering (NMKFCM) and Kernel Density Estimator methods used and automatic bottomup technique is used for pancreas subgrouping in C.T. scans. Top-Dow approach accuracy rate is less than bottom-up approach. Top-down approach required less time period as compare to bottom-up approach. In top-down approach, input image manually selected and processed it. KDE, NMKFCM and SIFT are used to detect feature of image. NMKFCM works on neighborhood point value. In KDE, Edge detection based on the kernel estimation of the probability density function .In SIFT, comprehensive information of local feature of image is focused.

A Forest Change Detection using auto Regressive Model-Based Kernel Fuzzy Clustering

Satellite images are used for applications related to the forest change detection, forest cover management, and so on as remote sensing provides the rich source of information for change detection. In this paper, the vegetation indices play a major role in extracting the useful information from the satellite images and the commonly employed indices. This paper analyzes the imagery data from the remote sensing satellites for detecting the changes in the forest over the year’s 2007-2017 using the pixel-based Bhattacharya distance. The indices from the satellite images are fed to the automatic segmentation model using the proposed Kernel Fuzzy Auto regressive (KFAR) model, which is the modified Kernel Fuzzy C-Means (KFCM) Clustering algorithm with the Conditional Autoregressive Value at Risk (CAVIAR). The forest change detection using the pixel-based Bhattacharya distance follows the segmentation, and the experimentation reveals that the proposed method acquired the minimal MSE and maximal accuracy of 0.0581 and 0.9211.

Kernel-Distance-Based Intuitionistic Fuzzy c-Means Clustering Algorithm and Its Application

Image segmentation plays an important role in machine vision, image recognition, and imaging applications. Based on the fuzzy c-means clustering algorithm, a kernel-distance-based intuitionistic fuzzy c-means clustering (KIFCM) algorithm is proposed. First, a fuzzy complement operator is used to generate the membership degree whereby the hesitation degree of intuitionistic fuzzy set is generated; second, a kernel-induced function is used to calculate the distance from each point to the cluster center instead of the Euclidean distance; third, a new objective function that includes the hesitation degree is established, and the optimization of the objective function results in new iterative expressions for the membership degree and the cluster center. The proposed KIFCM algorithm is compared with the fuzzy c-means clustering (FCM) algorithm, the kernel fuzzy c-means clustering (KFCM) algorithm, and the intuitionistic fuzzy c-means clustering (IFCM) algorithm in segmenting five images. The experimental results verify the effectiveness and superiority of our proposed KIFCM algorithm.

Prediction of phosphorylation sites based on granular support vector machine

Protein phosphorylation is the most extensive and important post-translational modification in eukaryotes, regulating the activity of almost all cells. Experimental methods used to identify phosphorylation sites, such as mass spectrometry, are costly and time-consuming. A number of algorithms have been developed to predict phosphorylation sites. However, they often select small data volume by random sampling. This cannot make full use of the characteristics of the entire data set to build a prediction model. According to the granularity calculation combined with the kernel fuzzy C-means clustering, this paper maps the massive raw data to a high-dimensional kernel space, and then divides the grains by clustering to obtain high-dimensional equilibrium grains. In particular, a specific granular support vector machine (KFCC–GSVM) prediction model is built in equilibrium grain data. This novel model improves the rationality and reliability of phosphorylation site data compression, so that the compressed data has the same distribution in the kernel space as the pre-compression data when applying the traditional SVM algorithm classification. Experimental results demonstrate that our method is better than the SVM-based non-kinase-specific phosphorylation site prediction method—Musite and the traditional GSVM method.

Kernel Fuzzy Clustering with Output Layer Self-Connection Recurrent Neural Networks for Software Cost Estimation

In the current world, the software cost estimation problem has been resolved using various newly developed methods. Significantly, the software cost estimation problems can be dealt with effectively with the recently grown recurrent neural network (RNN) than the other newly developed methods. In this paper, an improved approach is proposed to software cost estimation using Output layer self-connection recurrent neural networks (OLSRNN) with kernel fuzzy c-means clustering (KFCM). The proposed OLSRNN method follows the basics of traditional RNN models for integrating self-connections to the output layer; thereby, the output temporal dependencies are better captured. Also, the performance of neural networks is improved using the kernel fuzzy clustering algorithm to enhance software estimation results. Ultimately, five publicly available software cost estimation datasets are adapted to verify the efficacy of the proposed KFCM-OLSRNN method using the validation metrics such as MdMRE, PRED (0.25) and MMRE. The experimental results proved the efficiency of the proposed method for solving the software cost estimation problem.

Scalable Anomaly Detection for Large-Scale Heterogeneous Data in Cloud Using Optimal Elliptic Curve Cryptography and Gaussian Kernel Fuzzy C-Means Clustering

In most systems, a smart functionality is enabled through an essential vital service such as detecting anomalies from complex, large-scale and dynamic data. However, ensuring the privacy and security for the cloud data is the most crucial and challenging task in the present world. Moreover, it is important to safeguard the security of sensitive data and its privacy from unauthorized parties who are trying to access the data. Therefore, to accomplish this task, several encryption, decryption and key generation mechanisms were introduced in the existing works for privacy preserving in cloud platform. But, there still remain open issues such as increased communication overhead, reduced security and increased time consumption. Also, these existing works followed the symmetric key cryptographic mechanism for privacy preservation of data; hence, a single secret key is shared by several users for accessing the original data. Due to this fact, a high security risk arises and it allows unauthorized parties to access the data. Thus, this work introduces a cloud-based privacy preserving model for offering a scalable and reliable anomaly detection service for sensor data through holding the benefits of cloud resources. Also, this paper aims to impose a newly developed Elliptic Curve Cryptography-based Collective Decision Optimization (ECDO) approach over the proposed framework for improving the privacy and security of the data. Furthermore, to perform the data clustering computation we used the Gaussian kernel fuzzy [Formula: see text]-means clustering (GKFCM) algorithm within the cloud platform, especially for data partitioning and to classify the anomalies. Thus, the computational difficulties are limited by adopting this suitable privacy preserving model which collaborates a private server and a set of public servers through a cloud data center. Moreover, on encrypted data the granular anomaly detection operations are performed by the virtual nodes executed over public servers. Experimental validation was performed on four datasets resulting from Intel Labs publicly available sensor data. The experimental outcomes demonstrate the ability of the proposed framework in providing high anomaly detection accuracy without any degradation in data privacy.

A Combined Approach of Retinex & Spatial Kernel Fuzzy C-Means Clustering for Detection of Oil Spills in Satellite Imagery

Satellite images published by NASA Earth Observatory mainly affected with low contrast and detecting of oil spillage is quite cumbersome. Here, in this paper a novel approach for detection of oil spills in satellite imagery using Retinex and Spatial Kernel Fuzzy C-Means (SKFCM) Clustering is presented. Firstly the images are enhanced using retinex algorithm which gives more details within an image. Spatial information of enhanced images is been incorporated to kernel fuzzy membership functions for clustering thereby forming different clusters of oil spill and other regions. The results obtained show the profoundness of this approach.

Analysis on Integrated Energy System Cascading Failures Considering Interaction of Coupled Heating and Power Networks

As the coupling and integration of multi-energy flow in the integrated energy system (IES) deepen increasingly, the cascading failure will develop across different energy systems more easily and widely through the energy hub (EH). And it brings great challenges to the security and reliability of IES. The defects of the present cascading failure model of IES have been summarized, and a novel search strategy of fault chains in IES combined heating and power network was proposed in this paper. First, the initial risk assessment index of each energy branch is proposed to form the initial fault sets. Then, combined heat and power control (CHPC) strategies are introduced to deal with the branch overload conditions during the cascading failure. What is more, in order to reduce the workload and overcome the limitation of present methods, we analyzed the relevance of the branches to be predicted by using the kernel fuzzy C-means (KFCM) clustering algorithm and select the branches with the highest value of relevance as the subsequent failure. Based on the predicted fault chain, vulnerability analysis is presented to locate critical component and find out the correlation between cascading outages. The comprehensive evaluation index is also established to effectively evaluate the impact severity of cascading failure. Finally, the case studies are carried out on the combined heating and power systems to demonstrate the effectiveness of the proposed method.

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.

An efficient classification approach for detection of Alzheimer’s disease from biomedical imaging modalities

The complex patterns of the neuroimaging data are analyzed successfully with bio-medical imaging applications. The patients with/without AD can be discriminated effectively through several biomedical imaging modalities such as, sMRI, fMRI, PET and so on. In this paper, brain images from structural MRI (sMRI) are used for better categorization of 3 subjects namely, NC (Normal Control), MCI (Mild Cognitive Impairment) and AD (Alzheimer’s disease). Moreover, ambiguous training data employed for the discrimination of subjects may mislead the classifier to take incorrect decisions and in turn degrades the classification performance. In order to recover these hurdles, we propose an automated reliable system for the detection of AD affected patients accurately from the brain images of sMRI. The proposed system is a multi-stage system comprising four key phases namely, i) pre-processing, ii) feature extraction, iii) feature selection and iv) detection phase. In the initial phase, ROI regions related to Hippocampus (HC) and Posterior Cingulate Cortex (PCC) from the brain images are extracted using Automated Anatomical Labeling (AAL) method. In the feature extraction stage, important texture and shape features are extracted from HC and PCC involved in three brain planes. Nearly, 19 highly relevant AD related features are selected through a multiple-criterion feature selection method. It should be noted that, the class labels when explored manually consumes more time and turns to be an expensive process. Therefore, it is essential to construct an automatic method to identify irrelevant samples in the training data to enhance the decision-making process. With this in mind, at the detection phase, a novel classification technique is proposed by combining the Kernel fuzzy c-means clustering (i.e. unsupervised learning technique) and Back-propagation artificial neural network (i.e. supervised learning technique) to categorize NC, MCI and AD from the brain images of sMRI. This proposed KFCM based BPANN algorithm can improve the classification performance by removing the suspicious training samples. The proposed frameworks efficiency is evaluated with the ADNI subset and then to the Bordeaux-3 city dataset. The experimental validation of our proposed approach attains an accuracy of 97.63%, 95.4%, 96.4% for the most challenging classification tasks AD vs NC, MCI vs NC and AD vs MCI, respectively.