Improved Fuzzy C-means Clustering Research Articles

Adaptive load balancing in distributed cloud environment: Hybrid Kookaburra-Osprey optimization algorithm

A distributed cloud environment is characterized by the dispersion of computing resources, services, and applications across multiple locations or data centres. This distribution enhances scalability, redundancy, and resource utilization efficiency. To optimize performance and prevent any single node from becoming a bottleneck, it is imperative to implement effective load-balancing strategies, particularly as user demands vary and certain nodes experience increased processing requirements. This research introduces an Adaptive Load Balancing (ALB) approach aimed at maximizing the efficiency and reliability of distributed cloud environments. The approach employs a three-step process: Chunk Creation, Task Allocation, and Load Balancing. In the Chunk Creation step, a novel Improved Fuzzy C-means clustering (IFCMC) clustering method categorizes similar tasks into clusters for assignment to Physical Machines (PMs). Subsequently, a hybrid optimization algorithm called the Kookaburra-Osprey Updated Optimization Algorithm (KOU), incorporating the Kookaburra Optimization Algorithm (KOA) and Osprey Optimization Algorithm (OOA), allocates tasks assigned to PMs to Virtual Machines (VMs) in the Task Allocation step, considering various constraints. The Load Balancing step ensures even distribution of tasks among VMs, considering migration cost and efficiency. This systematic approach, by efficiently distributing tasks across VMs within the distributed cloud environment, contributes to enhanced efficiency and scalability. Further, the contribution of the ALB approach in enhancing the efficiency and scalability of distributed cloud environments is evaluated through analyses. The KBA is 1189.279, BES is 629.240, ACO is 1017.889, Osprey is 1147.300, SMO is 1215.148, APDPSO is 1191.014, and DGWO is 1095.405, respectively. The resource utilization attained by the KOU method is 1224.433 at task 1000.

Temperature-Sensitive Points Optimization of Spindle on Vertical Machining Center with Improved Fuzzy C-Means Clustering

The heat generated by motors and bearings of machine tools has a significant impact on machining accuracy. Error modeling and compensation has proven to be effective ways to reduce thermal errors and improve accuracy. An improved fuzzy c-means (FCM) clustering algorithm is proposed to determine the optimized temperature sensitive points for thermal error modeling of a spindle on the vertical machining center. The sensors are deployed to measure the temperature of different positions of machine tools, and the improved FCM algorithm is used to classify the measured data. Combined with the F-test statistics of multiple linear regression, the optimal temperature points of each group are selected. The improved FCM clustering algorithm significantly reduces the multicollinearity problem among temperature measuring points and avoids them falling into local optimization. The modeling method was verified through experiments on two types of vertical machining centers. The results show that the accuracy of the spindle in Y and Z directions of the machine tools was increased by more than 75%, and the model has good robustness, demonstrating application prospects in the selection of temperature measuring points of the spindle system of vertical machining centers.

RETRACTED ARTICLE: Comparative analysis of improved FCM algorithms for the segmentation of retinal blood vessels

The main purpose of identifying and locating the retina vessels is to specify from the fundus image the various tissues of the vascular structure, which can be wide or tight. The classification of vessels in the retinal image often confronts several challenges, such as the low contrast accompanying the fundus image, the inhomogeneity of the background lighting, and the noise. Moreover, fuzzy c-means (FCM) is one of the most frequently used algorithms for medical image segmentation due to its effectiveness. Hence, many FCM method derivatives have been developed to improve their noise robustness and time-consuming. This paper aims to analyze the performance of some improved FCM algorithms to recommend the best ones for the segmentation of retinal blood vessels. Eight derivatives of FCM algorithm are detained in this study: FCM, EnFCM, SFCM, FGFCM, FRFCM, DSFCM_N, FCM_SICM and SSFCA. The performance analysis is conducted from three viewpoints: noise robustness, blood vessels segmentation performance, and time-consuming. At first, the noise robustness of improved FCM clustering algorithms is evaluated using a synthetic image degraded by various types and levels of noise. Then, the ability of the selected algorithms to segment retinal blood vessels is assessed based on images from the DRIVE and STARE databases after a pre-processing phase. Finally, the time consumption of each algorithm is measured. The experiments demonstrate that the FRFCM and DSFCM_N algorithms achieve better results in terms of noise robustness and blood vessels segmentation. Regarding the running time, the FRFCM algorithm requires less time than other algorithms in the segmentation of retinal images. The results of this study are extensively discussed, and some suggestions are proposed at the end of this paper.

Data Processing Method of Mine Wind Speed Monitoring Based on an Improved Fuzzy C-Means Clustering Algorithm

Analyzing and processing mine wind speed monitoring data is the key to realizing intelligent ventilation and real-time calculation of the ventilation network. According to the characteristics of the artificial regulation of a mine ventilation system, a local regression fuzzy C clustering algorithm is proposed in this paper, which combines local outlier processing with global air volume state analysis. Firstly, the algorithm uses the robust local weighted regression principle to analyze and preprocess the data locally, determines the risk degree of the abnormal data according to the identified times of outliers, determines the clustering number according to the clustering validity function, and analyzes the global air volume fluctuation according to the clustering results. The results show that most outliers are identified in data preprocessing. Still, the processing of dense outliers is weak, related to the window width setting and weighting multiple. The number of clusters can represent the fluctuation of the ventilation state and the pre-processed cluster centers are 4.4% lower than the original data because most of the outliers are higher than the average data. According to the law of air volume balance, the clustering results can pave the way for the global deduction of mine wind speed. There is an implicit relationship between data preprocessing and the clustering process, and when intensive outliers are not eliminated, they may be identified as separate clusters. The research of this paper points out the direction of mine wind speed data analysis, which can provide a theoretical basis for intelligent mine ventilation and real-time calculation of the ventilation network.

Improved fuzzy c-means clustering by varying the fuzziness parameter

• Clustering results of vFCM are more closer to the minimum of the k means objective. • vFCM provides reduced sensitivity to initialization. • vFCM improves the clustering performance in high dimensions. • vFCM does not need careful tuning as FCM. • Experimental results of vFCM are similar to or better than k means and FCM. Fuzzy c -means (FCM) is one of the most frequently used methods for clustering, where the fuzziness weighting exponent m is a key hyper-parameter that directly affects the clustering performance. However, FCM requires careful tuning the fuzziness parameter which results in significant time costs. In this research, an improved FCM clustering by v arying the fuzziness parameter, called vFCM, is proposed to overcome this issue, based on the facts that the FCM objective is easy to optimize when m is large, while more local valleys appear as m decreases, hence the optimization problem presents a search process from simple to complex when m varies from a large value to a small value approaching 1. Here, the nature of m is similar to the temperature parameter in the deterministic annealing , and moving along a sequence of the FCM objectives by a linear method that proposes to update m automatically provides a form of annealing. Extensive experiments on simulated and real-world data sets show that vFCM is not only more robust to initialization but also improves the clustering performance in high dimensions. Furthermore, the clustering results of vFCM have a low fluctuation according to different m , so it does not require careful tuning the fuzziness parameter. The time that vFCM takes is greatly reduced.

Image Enhancement Method based on an Improved Fuzzy C-Means Clustering

Image enhancement is an important method in the process of image processing. This paper proposes an image enhancement method base on an improved fuzzy c-means clustering. The method consists of the following steps: firstly, proposed a fuzzy c-means clustering with a cooperation center(FCM-co). Secondly, using the FCM-co, divide the image pixels into different clusters and marked membership values to those clusters. Thirdly, modify the membership values. Finally, calculate the new pixel gray levels. This enhancement method can overcome the disadvantage of overexposure and better retain image details. Through the experiment, the test results show that the proposed enhancement method could achieve better performance.

Morphological Reconstruction-Based Image-Guided Fuzzy Clustering with a Novel Impact Factor.

The guided filter is a novel explicit image filtering method, which implements a smoothing filter on “flat patch” regions and ensures edge preserving on “high variance” regions. Recently, the guided filter has been successfully incorporated into the process of fuzzy c-means (FCM) to boost the clustering results of noisy images. However, the adaptability of the existing guided filter-based FCM methods to different images is deteriorated, as the factor ε of the guided filter is fixed to a scalar. To solve this issue, this paper proposes a new guided filter-based FCM method (IFCM_GF), in which the guidance image of the guided filter is adjusted by a newly defined influence factor ρ. By dynamically changing the impact factor ρ, the IFCM_GF acquires excellent segmentation results on various noisy images. Furthermore, to promote the segmentation accuracy of images with heavy noise and simplify the selection of the influence factor ρ, we further propose a morphological reconstruction-based improved FCM clustering algorithm with guided filter (MRIFCM_GF). In this approach, the original noisy image is reconstructed by the morphological reconstruction (MR) before clustering, and the IFCM_GF is performed on the reconstructed image by utilizing the adjusted guidance image. Due to the efficiency of the MR to remove noise, the MRIFCM_GF achieves better segmentation results than the IFCM_GF on images with heavy noise and the selection of the influence factor for the MRIFCM_GF is simple. Experiments demonstrate the effectiveness of the presented methods.

Optimal Excess Commuting Evaluation Based on Local Minimal Costs

Excess commuting refers to the value of unnecessary commuting or distance costs. Traditional commuting distance models adapt the most efficient scenario with people working in the nearest workplace geographically. Even though there have been some attempts to include constraints with commuter attributes and neighborhood features, problems arise with traditional geographical space and the subjectivity of these predefined characteristics. In this paper, we propose a method to calculate theoretical local minimal costs, which considers preferences that are inherently behavioral based on current work–home trips in the process of reassigning the work–home configuration. Our method is based on a feature space with a higher dimension and with the enlargement of attributes and relations of and between commuters and neighborhoods. Additionally, our solution is arrived at innovatively by improved Fuzzy C-Means clustering and linear programming. Unlike traditional clustering algorithms, our improved method adapts entropy information and selects the initial parameters based on the actual data rather than on prior knowledge. Using the real origin–destination matrix, theoretical minimal costs are calculated within each cluster, referred to as local minimal costs, and the average sum of local minimal costs is our theoretical minimal cost. The difference between the expected minimal cost and the actual cost is the excess commuting. Using our method, experimental results show that only 13% of the daily commuting distance in Wuhan could be avoided, and the theoretical distance is approximately 1.06 km shorter than the actual commuting distance.

Automatic cloud segmentation from INSAT‐3D satellite image via IKM and IFCM clustering

Cloud extraction and classification from satellite imagery is important for many applications in remote sensing. Satellite images are segmented based on distance, intensity and texture of the images. The popular segmentation algorithms, k-means (KM) and fuzzy c-means (FCM) clustering algorithms, face some problems such as unknown number of groups, unknown initialization and dead centers. In this paper, an unsupervised pixel classification by the KM and FCM algorithms is improved and the selection of centroids is made automatic. The proposed improved k-means (IKM) and improved fuzzy c-means (IFCM) clustering algorithms segment the INSAT-3D satellite's thermal infrared image into low-level, middle-level, high-level clouds and non-cloudy region. As human beings can easily find the clouds in the satellite images, visible image is used to differentiate the clouds from the background. A threshold is found from the histogram of the visible image to separate the cloudy and non-cloudy pixels. The other three thresholds to divide the clouds into three types are found from the thermal infrared image's histogram. The segmentation results of IKM and IFCM algorithms are compared with the existing segmentation algorithms. The comparison shows that IFCM algorithm matches well with original image followed by IKM algorithm as compared with existing algorithms.

Automatic detection of HFOs based on singular value decomposition and improved fuzzy c-means clustering for localization of seizure onset zones

This paper devises a new detector based on singular value decomposition (SVD) and improved fuzzy c-means (FCM) clustering for automatically detecting high-frequency oscillations (HFOs) that are used for localizing seizure onset zones (SOZs) in epilepsy. First, HFO candidates (HFOCs) are obtained by the root mean square method. Next, a time-frequency analysis method is applied to eliminate spikes from HFOCs, which consists of the Stockwell transform, SVD combined with the k-medoids clustering algorithm, Stockwell inverse transform, and threshold method. Then, four kinds of distinctive features, i.e. mean singular values, line lengths, power ratios and spectral centroid of the rest of HFOCs, are extracted and augmented as feature vectors. These vectors are used as the input of the improved FCM clustering algorithm optimized by the simulated annealing algorithm combined with the genetic algorithm. Finally, the localization of SOZs is accomplished based on the concentrations of the detected HFOs. The superiority of the devised detector over other five existing ones is demonstrated by comparing their localization performance.

Application of Fuzzy C-Mean Clustering Based on Multi-Polar Fuzzy Entropy Improvement in Dynamic Truck Scale Cheating Recognition

In the big data background, the uncertainty of data is increasingly apparent. Multi-polar fuzzy feature of data has been more popularly used by the research community for the purpose of the classification of weighing cheating in dynamic truck scale characteristic and the clustering problem of multi-polar fuzzy feature information. Additionally, the traditional classification method leads to slow speed and inaccuracy because of its difficulties. Therefore, by considering a multi-polar fuzzy feature classification of defects, a fuzzy c-means ( FCM) clustering algorithm based on multi-polar fuzzy entropy is proposed. Firstly, according to the evaluation of available characteristics, the characteristic value of clustering samples is established. Secondly, we calculated the multi-polar fuzzy entropy of clustering samples. Finally, an improved FCM clustering algorithm based on multipolar fuzzy entropy is presented. The experimental results of the data set collected from 5 different types of weighing cheating cars demonstrate that the algorithm improves the classification accuracy of FCM with multi-polar fuzzy feature information clustering and reduces significantly both the number of iterations and the classification time.

An adaptive clustering and classification algorithm for Twitter data streaming in Apache Spark

On-going big data from social networks sites alike Twitter or Facebook has been an entrancing hotspot for investigation by researchers in current decades as a result of various aspects including up-to-date-ness, accessibility and popularity; however anyway there may be a trade off in accuracy. Moreover, clustering of twitter data has caught the attention of researchers. As such, an algorithm which can cluster data within a lesser computational time, especially for data streaming is needed. The presented adaptive clustering and classification algorithm is used for data streaming in Apache spark to overcome the existing problems is processed in two phases. In the first phase, the input pre-processed twitter data is viably clustered utilizing an Improved Fuzzy C-means clustering and the proposed clustering is additionally improved by an Adaptive Particle swarm optimization (PSO) algorithm. Further the clustered data streaming is assessed utilizing spark engine. In the second phase, the input pre-processed Higgs data is classified utilizing the modified support vector machine (MSVM) classifier with grid search optimization. At long last the optimized information is assessed in spark engine and the assessed esteem is utilized to discover an accomplished confusion matrix. The proposed work is utilizing Twitter dataset and Higgs dataset for the data streaming in Apache Spark. The computational examinations exhibit the superiority of presented approach comparing with the existing methods in terms of precision, recall, F-score, convergence, ROC curve and accuracy.

Edge Detection of Images Using Improved Fuzzy C-Means and Artificial Neural Network Technique

Edge detection (ED) is an embryonic development, which is essential for any intricate image processing and recognition undertaking. This paper proposed another system to upgrade the method and Artificial neural network for speaking to vulnerability in the image slopes and collection. The vulnerability in the image inclination distinguishes the genuine edges which might be overlooked by other systems. This e is valuable in the field of restorative imaging applications, for example, MRI division, cerebrum tumor, filtering and so on. Attractive reaction imaging connected in restorative science to analyze tumors in body parts by creating great images of within the human body, by utilizing different edge identifiers. There exist many edge finders yet at the same time, requirement for inquire about is felt improve their execution. And furthermore, this paper distinguishes the edges in the broken bones, edge ID, satellite edge detection ID. An exceptionally basic issue looked by many edge finders is the decision of limit esteems. This paper presents fuzzy and ANN based edge detection utilizing Improved Fuzzy C-means clustering (FCM) strategy. Enhanced FCM approach is utilized in producing different gatherings which are then contribution to the Mamdani fuzzy surmising framework. In this, we are utilizing versatile middle separating for evacuating commotion; this strategy adequately expels the clamor and gives better outcomes. This entire procedure results in the age of the limit parameters which is then encouraged to the established sobel edge locator which helps in improving its edge detection capacity utilizing the fuzzy logic. This entire setup is connected to Images. The recovered outcomes express to that fuzzy and ANN based Improved Fuzzy C-means clustering enhances the introduction of customary sobel edge identifier in associate with retentive information around the tumors of the mind.

Automatic Human Brain Tumor Detection in MRI Image Using Template-Based K Means and Improved Fuzzy C Means Clustering Algorithm

In recent decades, human brain tumor detection has become one of the most challenging issues in medical science. In this paper, we propose a model that includes the template-based K means and improved fuzzy C means (TKFCM) algorithm for detecting human brain tumors in a magnetic resonance imaging (MRI) image. In this proposed algorithm, firstly, the template-based K-means algorithm is used to initialize segmentation significantly through the perfect selection of a template, based on gray-level intensity of image; secondly, the updated membership is determined by the distances from cluster centroid to cluster data points using the fuzzy C-means (FCM) algorithm while it contacts its best result, and finally, the improved FCM clustering algorithm is used for detecting tumor position by updating membership function that is obtained based on the different features of tumor image including Contrast, Energy, Dissimilarity, Homogeneity, Entropy, and Correlation. Simulation results show that the proposed algorithm achieves better detection of abnormal and normal tissues in the human brain under small detachment of gray-level intensity. In addition, this algorithm detects human brain tumors within a very short time—in seconds compared to minutes with other algorithms.

Classifying the traffic state of urban expressways: A machine-learning approach

The classification of the urban traffic state and its application is an important part of intelligent transportation systems (ITS), which can not only help traffic managers grasp the traffic operation situation and analyze congestion, but also provide travelers with more traffic information and help them avoid congestion. Thus, an accurate traffic state classification method would be very practical for urban traffic management. The primary objective of this study is to classify the urban traffic state using a machine-learning method (i.e., the FCM clustering method, and the classification results can be determined from the corresponding clustering labels). In this approach, two parts are developed. First, a new classification indicator, i.e., the ample degree of road network is proposed, and it will make up a comprehensive classification indicator system with other parameters such as traffic flow, speed and occupancy. Then, the traditional fuzzy c-means (FCM) clustering approach is improved in two regards, i.e., the fuzzy membership function improvement and weighting processing of the samples, and these improvements can enhance the clustering performance. As a result, an improved machine-learning method (i.e., the improved FCM clustering approach) is developed and used to conduct the clustering analysis with real-world traffic flow data. Next, a case study of Shanghai is used to guide the study process, which consists of data processing, clustering analysis and method comparison. The other methods (e.g., the support vector machines (SVM) method, the decision tree method, the k-Nearest Neighbor (KNN) method and the traditional FCM clustering approach) are introduced to compare with the improved FCM clustering approach. The discussion shows the superiority of the proposed method (e.g., compared with the traditional FCM clustering approach, the objective function value of the improved method decreased by 31.11%, and cluster center error also show a descending trend), and it outperforms the other methods in classification performance (e.g., the overall classification accuracy of the improved FCM method increased by 10.10%, 5.45%, 30.92% and 35.66% in comparison with the traditional FCM method, SVM method, decision tree method and KNN method, respectively). Additionally, the NMI, ROC curve results also illustrated the superiority of the improved FCM method to other methods. These comaprison results suggest that the improved FCM clustering approach is feasible and the results can be well used in the advanced traffic management system, which may have the potential to serve as a reference for releasing accurate traffic state information and preventing traffic congestion and risk.

Mixing Matrix Estimation of Underdetermined Blind Source Separation Based on Data Field and Improved FCM Clustering

In modern electronic warfare, multiple input multiple output (MIMO) radar has become an important tool for electronic reconnaissance and intelligence transmission because of its anti-stealth, high resolution, low intercept and anti-destruction characteristics. As a common MIMO radar signal, discrete frequency coding waveform (DFCW) has a serious overlap of both time and frequency, so it cannot be directly used in the current radar signal separation problems. The existing fuzzy clustering algorithms have problems in initial value selection, low convergence rate and local extreme values which will lead to the low accuracy of the mixing matrix estimation. Consequently, a novel mixing matrix estimation algorithm based on data field and improved fuzzy C-means (FCM) clustering is proposed. First of all, the sparsity and linear clustering characteristics of the time–frequency domain MIMO radar signals are enhanced by using the single-source principal value of complex angular detection. Secondly, the data field uses the potential energy information to analyze the particle distribution, thus design a new clustering number selection scheme. Then the particle swarm optimization algorithm is introduced to improve the iterative clustering process of FCM, and finally get the estimated value of the mixing matrix. The simulation results show that the proposed algorithm improves both the estimation accuracy and the robustness of the mixing matrix.

An Improved Fuzzy C-Means Clustering Algorithm Based on Multi-chain Quantum Bee Colony Optimization

The fuzzy c-means (FCM) algorithm is the most popular clustering method. Many studies of FCM had been done. However, the FCM algorithm and its studies are usually affected by the selection of initial values and noise data, and can easily fall into local optimal value. To overcome these drawbacks of FCM, this paper proposed the algorithm of FCM based on multi-chain quantum bee colony algorithm (MQBC-FCM). In MQBC-FCM, first, the multiple chains encoding method is introduced to the artificial bee colony algorithm to propose the MQBC algorithm. Then MQBC is used to search for the optimal initial clustering centers. The proposed algorithm is used on artificial data sets and image segmentations, and its performance is contrasted with several algorithms. The experimental results have indicated that the proposed MQBC-FCM has efficiently improved the performance of the clustering algorithm.

An Improved Fuzzy C-Means Clustering Algorithm Based on Potential Field

In the area of recommendation system, clustering is an effective way to find similar users and reduce the complexity of recommendation algorithm. A good clustering result has two characteristics: maximizing compactness within cluster and maximizing separation between clusters. Following laws of attraction and repulsion, electric charges in an electric potential field have the same feature as clusters. Based on potential field, an improved Fuzzy C-Means (FCM) clustering algorithm is proposed, which is called EFCM (Electrical FCM). This algorithm combines the basic electrical rules of potential field, Coulomb's law and data field theory to obtain better clustering results. The experiment results show that the improved EFCM algorithm not only obtains good initial cluster centers, but also helps to improve the process of iterative updating.

On urban road traffic state evaluation index system and method

Traffic state evaluation is a basic and critical work in the research on road traffic congestion. It can provide basic data support for the improvement measures and information release in traffic management and service. The aim of this research is to obtain a comprehensive value to describe traffic state accurately based on the evaluation index system. In this paper, it is carried out using fuzzy c-means (FCM) algorithm and fuzzy entropy weight method. In the framework, traffic flow was classified into six different states to determine the fuzzy range of indices using the improved FCM clustering analysis. Besides, fuzzy entropy weight method is proposed to compute the evaluation result of traffic state for section, road and road network, respectively. The experiments based on the traffic information in a subset of Beijing’s road network prove that the findings of traffic evaluation are in accordance with the actual situation and people’s sense of traffic state.

An Improved Fuzzy C-Means Clustering Algorithm and Application in Meteorological Data

The fuzzy clustering algorithm is sensitive to the m value and the degree of membership. Because of the deficiencies of traditional FCM clustering algorithm, we made specific improvement. Through the calculation of the value of m, the amendments of degree of membership to the discussion of issues, effectively compensate for the deficiencies of the traditional algorithm and achieve a relatively good clustering effect. Finally, through the analysis of temperature observation data of the three northeastern province of china in 2000, the reasonableness of the method is verified.