C-means Clustering Algorithm Research Articles

Study on the Application of Ganan Hakka Architectural Elements in Modern Architectural Interior Design

Abstract This paper constructs a morphological extraction model of Ganan Hakka architectural elements based on feature fusion using ResNet as the backbone network. Then, the extraction method of Ganan Hakka architectural colors based on the combination of multivariate fuzzy C-mean clustering algorithm and the K-mean++ algorithm is designed. The improved recommendation algorithm intelligently matches the extracted color and morphological elements and applies them to modern architectural interior design. The results show that the average time of interior design intelligent recommendation is 3.8s, the overall accuracy is above 0.94, and the degree of interior design intelligent matching is in the range of [0.78,0.99]. Based on this study, Ganan Hakka’s architectural elements can be well combined with modern architectural interiors to promote traditional culture.

Unsupervised Multiview Fuzzy C-Means Clustering Algorithm

The rapid development in information technology makes it easier to collect vast numbers of data through the cloud, internet and other sources of information. Multiview clustering is a significant way for clustering multiview data that may come from multiple ways. The fuzzy c-means (FCM) algorithm for clustering (single-view) datasets was extended to process multiview datasets in the literature, called the multiview FCM (MV-FCM). However, most of the MV-FCM clustering algorithms and their extensions in the literature need prior information about the number of clusters and are also highly influenced by initializations. In this paper, we propose a novel MV-FCM clustering algorithm with an unsupervised learning framework, called the unsupervised MV-FCM (U-MV-FCM), such that it can search an optimal number of clusters during the iteration process of the algorithm without giving the number of clusters a priori. It is also free of initializations and parameter selection. We then use three synthetic and six benchmark datasets to make comparisons between the proposed U-MV-FCM and other existing algorithms and to highlight its practical implications. The experimental results show that our proposed U-MV-FCM algorithm is superior and more useful for clustering multiview datasets.

Machine Learning and Fuzzy C-Means Clustering for the Identification of Tomato Diseases

Diseases in tomato plants can cause economic losses in the agricultural industry. Identification of tomato plant diseases is important to choosing the right action to control their spread. In this research, we propose an approach to identify tomato plant diseases using a machine learning algorithm and lab colour space-based image segmentation using the fuzzy c-means (FCM) clustering algorithm. The segmentation method aims to separate the infected area, leaf image, and background in the tomato plant image. In the first step, the tomato image is represented in the Lab colour space, which allows for combining information on brightness (L), red-green colour components (a), and yellow-blue colour components (b). Then, the FCM algorithm is applied to segment the image. The segmentation results are then evaluated through an identification process using machine learning techniques such as k-Nearest Neighbors (kNN), Random Forest (RF), Support Vector Machine (SVM), and Naïve Bayes (NB) to measure the level of accuracy. The dataset used in this research is tomato images, which include various plant diseases obtained from the Kaggle dataset. The performance results of the proposed method show that the segmentation approach based on Lab colour space with the FCM clustering algorithm is able to identify infected areas well. The accuracy value of each machine learning method used is kNN of 85.40%, RF of 88.87%, SVM of 80.73%, and NB of 74.60%. The proposed method shows success in accurately identifying types of tomato plant diseases and obtains improvements compared to without using segmentation.

IMI2: A fuzzy clustering validity index for multiple imbalanced clusters

The fuzzy c-means (FCM) clustering algorithm requires the pre-definition of the number of clusters. When used for imbalanced datasets, FCM tends to equalize the sizes of clusters and thus produces bad clustering results. Although clustering validity index (CVI) is commonly employed to determine the number of clusters, it is often ineffective for bad clustering results of FCM. To address this issue, we have previously proposed a fuzzy CVI, namely IMI, for imbalanced datasets. However, IMI encounters issues when the number of clusters exceeds three, as it tends to divide the majority clusters into multiple subclusters. In this paper, a novel CVI, IMI2, is proposed to enhance the performance of IMI. IMI2 adopts a two-step clustering algorithm that merges some clusters from the IMI result based on their separation values. This enhancement overcomes the limitations of IMI, rendering IMI2 an excellent CVI capable of accurately determining the number of clusters for datasets with multiple imbalanced clusters. The performance of IMI2 is evaluated using several synthetic and UCI datasets. The results demonstrate that IMI2 outperforms other methods in terms of its superior performance for multiple imbalanced clusters. IMI2 retains the good performance of IMI for datasets with two or three clusters, while significantly improving it for multiple imbalanced clusters.

Performance Improvement of Fuzzy C-Means Clustering Algorithm by Optimized Early Stopping for Inhomogeneous Datasets

Performance Improvement of Fuzzy C-Means Clustering Algorithm by Optimized Early Stopping for Inhomogeneous Datasets

Hybrid COOT-Reverse Cognitive Fruit Fly Optimization-Based Big Data Services and Virtual Machine Allocation for Cloud Storage System

In recent years, cloud computing technologies have been developed rapidly in this computing world to provide suitable on-demand network access all over the world. A cloud service provider offers numerous types of cloud services to the user. But the most significant issue is how to attain optimal virtual machine (VM) allocation for the user and design an efficient big data storage platform thereby satisfying the requirement of both the cloud service provider and the user. Therefore, this paper presents two novel strategies for optimizing VM resource allocation and cloud storage. An optimized cloud cluster storage service is introduced in this paper using a binarization based on modified fuzzy c-means clustering (BMFCM) algorithm to overcome the negative issues caused by the repetitive nature of the big data traffic. The BMFCM algorithm utilized can be implemented transparently and can also address problems associated with massive data storage. The VM selection is optimized in the proposed work using a hybrid COOT-reverse cognitive fruit fly (RCFF) optimization algorithm. The main aim of this algorithm is to improve the massive big data traffic and storage locality. The CPU utilization, VM power, memory dimension and network bandwidth are taken as the fitness function of the hybrid COOT-RCFF algorithm. When implemented in CloudSim and Hadoop, the proposed methodology offers improvements in terms of completion time, overall energy consumption, makespan, user provider satisfaction and load ratio. The results show that the proposed methodology improves the execution time and data retrieval efficiency by up to 32% and 6.3% more than the existing techniques.

Generalized Ordered Intuitionistic Fuzzy C-Means Clustering Algorithm Based on PROMETHEE and Intuitionistic Fuzzy C-Means

The problem of ordered clustering in the context of decision-making with multiple criteria has garnered significant interest from researchers in the field of management science and operational research. In real-world scenarios, the datasets often exhibit imprecision or uncertainty, which can lead to suboptimal ordered-clustering outcomes. However, the intuitionistic fuzzy c-means (IFCM) clustering algorithm enhances the accuracy and effectiveness of decision-making processes by effectively handling uncertain dataset information for clustering. Therefore, we propose a new clustering algorithm, called the generalized ordered intuitionistic fuzzy c-means (G-OIFCM), based on PROMETHEE and the IFCM clustering algorithm. Different from the classical IFCM clustering algorithm, we use positive flow ( φ + s i ∈ 0 , 1 ) and negative flow ( φ − s i ∈ 0 , 1 ) of PROMETHEE to generate ordered clusters within the intuitionistic environment. We define a new objective function based on the positive and negative flow of the PROMETHEE and IFCM clustering algorithm, whose properties are mathematically justified in terms of convergence and optimization. The performance of the proposed algorithm is evaluated using two different real-world datasets to assess both the ordered clustering and the quality of partitioning. To demonstrate the effectiveness of G-OIFCM, a comparison is conducted with three other algorithms: fuzzy c-means (FCM), ordered fuzzy c-means (OFCM), and an adaptive generalized intuitionistic fuzzy c-means (G-IFCM). The results demonstrate the effectiveness of G-OIFCM in enhancing optimal ordered clustering and utility when dealing with uncertainty in datasets.

Detection of types cyber-bullying using fuzzy c-means clustering and xgboost ensemble algorithm

In this study a neural network model (XGB_CTD) that will prediction which type of bullying the users may expose to, through dataset gained by the cyberbullying scale applied to the young internet users is formulated. Extreme Gradient Boosting (XGboost) algorithm, one of the ensemble learning methods is used in this method. There while this model contains 13 input parameters taken from the scale, there exist one output parameter classified one of the 9 outputs. The reliability of the data set obtained through survey is confirmed by statistical methods. Data set has been fragmented with Fuzzy C-Means (FCM) which is one of fuzzy clustering algorithms. Hyper-parameters for the maximum efficiency of the model training have been defined as model, learning and boosting method. Independent variables in data set have been scaled through standard normalization. As a result, the model has yielded % 91,75 accuracy rate in prediction of the classification as 9 different cyberbullying types. The same data set has been trained by different machine learning algorithms. It is seen that the proposed model has reached the highest accuracy when compared to the conventional machine learning algorithms. This study aims at prediction cyberbullying through the proposed model including different questions without claim by the young users as they were bullied. Similarly, type of the cyberbullying will also be able to be estimated by the help of internet using habits of the young users. Therefore, it is thought that the young can be prevented from experiencing psychological pressure or digital life fear.

Research on Daily Load Curve Classification Based on Improved Fuzzy C-means Clustering Algorithm

For the problem of severe unpredictability and three-phase unbalance of user demand in low-voltage distribution networks, a daily load curve clustering technique integrating the sparrow search algorithm (SSA) and the fuzzy C-mean clustering algorithm (FCM) is presented. The initial load data set is pre-processed to lessen the interference of classification results. The load characteristic index of the daily load curve is removed to produce a reduced dimensional data set. The clustering validity index is introduced to solve the optimal number of clusters, and the early warning mechanism of the sparrow search algorithm is adopted to improve the global search capability. These improvements are made to improve the sensitivity of the initial clustering center of the FCM algorithm and the problem of local optimum in the process of finding the optimum. The simulation used to validate the revised clustering algorithm’s accuracy and efficacy for daily load categorization is a reference for resolving the three-phase load unbalance issue.

Estimation of interlayer textural relationships to discriminate the benignancy/malignancy of brain tumors

A computer-aided diagnosis system is a popular tool to predict the risk factors of brain tumors. However, the existing techniques are unable to provide high detection accuracy due to the inability of capturing the hidden features of brain tumors. In this view, the present work decomposes the MR modality images into different layers using non-sub-sampled shearlet transformation (NSST). Following this, the proposed detection pipeline employs adaptive pulse-coupled neural network (A-PCNN) module and a fuzzy c-means (FCM) clustering algorithm for enhancement and segmentation of suspicious regions respectively. Interlayer textural relationships of tumors are estimated in terms of the layer-wise difference of the gray-level cooccurrence matrix (GLCM), similarity indices, and entropy distributions. Thenceforth, those feature coefficients are coded with binary sequences to express the textural homogeneity/randomness of tumors. Finally, these handcrafted multi-scale feature quantifiers are fed to some standard classifiers such as the k-nearest neighbor (kNN) technique, the linear square support vector machine (LS-SVM), and the decision tree (DT) for discriminating the state of benignancy/malignancy of tumors. Experimental results ensure that the proposed detection model shows superior performance compared to existing methods.

A combined fuzzy backtracking search optimization algorithm to localize retinal blood vessels for diabetic retinopathy

For diabetic retinopathy (DR) surgery, localization of retinal blood vessels is of paramount importance. Fundus images which are often used for DR diagnosis suffer from poor contrast (between the retinal background and the blood vessels, due to its size) limits the diagnosis. In addition to this, various pathological changes in retinal blood vessels may also be observed for different diseases such as glaucoma and diabetes. To alleviate, in this paper, an automated unsupervised retinal blood vessel segmentation technique, based on backtracking search optimization algorithm (BSA), is proposed. The BSA method is used to optimize the local search of fuzzy c-means clustering (FCM) algorithm to find micro-diameter sized vessels along with coarse vessels. The proposed technique is tested on two publicly available retinal datasets (i.e., STARE and DRIVE) and verified using the dataset collected from various hospitals in Bangalore and Mangalore, India. The results show that the performance of the proposed method is comparable to the conventional techniques in terms of sensitivity, specificity, and accuracy.

A federated learning framework based on transfer learning and knowledge distillation for targeted advertising.

The rise of targeted advertising has led to frequent privacy data leaks, as advertisers are reluctant to share information to safeguard their interests. This has resulted in isolated data islands and model heterogeneity challenges. To address these issues, we have proposed a C-means clustering algorithm based on maximum average difference to improve the evaluation of the difference in distribution between local and global parameters. Additionally, we have introduced an innovative dynamic selection algorithm that leverages knowledge distillation and weight correction to reduce the impact of model heterogeneity. Our framework was tested on various datasets and its performance was evaluated using accuracy, loss, and AUC (area under the ROC curve) metrics. Results showed that the framework outperformed other models in terms of higher accuracy, lower loss, and better AUC while requiring the same computation time. Our research aims to provide a more reliable, controllable, and secure data sharing framework to enhance the efficiency and accuracy of targeted advertising.

An Adaptive Fuzzy C-Means Noise Image Segmentation Algorithm Combining Local and Regional Information

Noisy image segmentation using fuzzy c-mean clustering (FCM) algorithm is an important direction in the field of image segmentation, among which FCM using local information or non-local information constraints has made some progress. However, as the noise density increases, the local information cannot restore the real image. The non-local information-based segmentation method overly retains the noise and thus produces false edges resulting in the degradation of image segmentation accuracy. To address these problems, this paper use the regional and local information to develop an adaptive FCM image segmentation algorithm(RLFCM). First, the regional information and local information are obtained by the regional information filtering method and the local information filtering method. Then the weighted image is constructed in a certain way. Secondly, to make the objective function adaptively adjust the constraint ratio of the original image and the weighted image pixel by pixel, the degree of constraint is adaptively changed by using the difference between them as the coefficients of both through certain transformation. Finally, to speed up the convergence of the objective function, we add the number of clustering samples to the denominator of the objective function by certain transformations to reduce the number of iterations of the objective function. The proposed algorithm achieves more than 90% in image segmentation accuracy (SA), and mean Intersection-over-Union (mIoU) through synthetic and real-world image tests and acquires good performance in other indexes.

Tumor Detection in Brain Using MRI Images

This work provides a robust segmentation approach that combines Template-based K-means with the modified Fuzzy C-means (TKFCM) clustering algorithm. Operator and equipment error is reduced. The template is chosen in this method based on the convolution of grey level intensity in a tiny area of a brain image and a brain tumor image. The K-means method emphasizes initial segmentation through template selection. Distances from the cluster centroid to the cluster data points are used to update membership until it is optimal. This Euclidian distance is determined by the coarse image's intensity, entropy, contrast, dissimilarity, and homogeneity which were solely based on resemblance in conventional FCM. Then, using updated membership and automatic cluster selection, a sharp segmented image with red indicated tumor is created using the improved FCM technique. TKFCM detects minor differences in grey level intensity between normal and diseased tissue. The TKFCM method's performance is analyzed using a neural network to produce better regression and less error. The performance parameters yield meaningful results that are helpful at finding cancers in several different intensity-based brain scans brain. Keywords - Magnetic Resonance Imaging (MRI), Revised Fuzzy C-Means Algorithms and Template based K-Mean Clustering (TKFCM), gray level intensity, coarse image, features selection, Artificial Neural Network (ANN).

Short-term PV Power Prediction Based on SAGA-FCM and XGBoost

The stable and economic operation of the power grid cannot be achieved without accurate prediction of PV plant power. To address the problem of unstable clustering quality of the fuzzy c-mean clustering algorithm, this paper proposes to combine the genetic algorithm and simulated annealing algorithm to optimize the FCM clustering algorithm, and then combine it with XGBoost to construct a short-term PV power generation prediction model. Firstly, the robustness of the clustering algorithm is improved. Secondly, the differences in power generation characteristics under different weather types are considered, and the prediction models under three typical weather types are separately trained. Finally, to verify the improvement of the model on prediction accuracy, different clustering methods are used to compare the prediction results, and the results show that the algorithm proposed in this paper has higher accuracy compared with the other three methods.

Fuzzy clustering algorithm for university students' psychological fitness and performance detection

Students' psychological fitness is unavoidable, hindering personal development, social interactions, peer influence, and adolescence. Academic stress may be the most dominant factor affecting college students' mental well-being. Therefore, improving the monitoring of mental health issues among college students is a vital topic for study. However, identifying the student's stress level is challenging, leading to uncertainty. Hence, this paper suggests Heuristic Fuzzy C-means Clustering Algorithm (HFCA) for analyzing college students' stress levels, psychological well-being and academic performance detection. The data are collected from the Kaggle stress dataset for predicting student mental health. This study investigates the psychological factors affecting students' academic performance using the suggested HFCA. Students' performance may be predicted using the Fuzzy Cognitive Map (FCM) in this study. This study used fuzzy clustering algorithms to discover the most crucial aspects of student success, such as student involvement and satisfaction. A better understanding of the risk factors for and protective factors against poor mental health can serve as the basis for developing policies and targeted interventions to prevent mental health problems and guarantee that at-risk students can access the help they need. The experimental analysis shows the proposed method HFCA to achieve a high student performance ratio of 96.7%, cognitive development ratio of 97.2%, student engagement ratio of 97.5% and prediction ratio of 95.1% compared to other methods.

Identification of Nonlinear Behavior of Bridge Structures Using Time Series Analysis of Vibration Signals

ABSTRACT One of the most widely used linear techniques for solving damage identification problems and prediction of the system dynamic behavior is modal testing and analysis. However, most of real structures exhibit nonlinear behavior during their lifetime caused by a number of various reasons. The presence of nonlinearity in a structural system changes its dynamic characteristics; hence, the use of linear techniques is improper for prediction of the system behavior. In this paper, a vibration-based nonlinearity identification technique was proposed to identify early changes in a dynamic system prior to significant damages. This technique combines the time series Autoregressive Moving Average with Exogenous Inputs (ARMAX) modelling, probability theory, and Fuzzy C-Means (FCM) clustering algorithm to categorize the linear and nonlinear dynamic behaviors of a dynamic system. The technique aims to categorize the linear and nonlinear behaviors of a structure, when it is subjected to various levels of excitation source. To show the validity of the proposed method, a series of shake table tests was performed on a six-span steel truss bridge model. The bridge model was excited using different amplitudes of ground motion to control the nonlinearity degree of rubber-based supports. It can be concluded from the analysis results that the new vibration-based nonlinearity identification technique was able to identify nonlinear behavior of the bridge model once it was subjected to different levels of earthquake excitations. This algorithm was able to categorize the linear and nonlinear behaviors of the bridge model and define a specific threshold for different excitation sources.

A Novel Method for Building Contour Extraction Based on CSAR Images

Circular synthetic aperture radar (CSAR) can obtain more complete scattering characteristics by observing the target with different azimuth angles. Therefore, extracting the complete structure of the target from CSAR images is of great significance for accurate interpretation. At present, the artificial target extraction based on CSAR images mostly uses anisotropic scattering features. For special targets such as buildings, as the walls and the ground form dihedral corner structures, there are also obvious strong scattering features such as double-scattering lines in SAR images. Therefore, combining the strong scattering features of buildings at specific aspects with anisotropic scattering characteristics at different aspects can obtain better extraction results, and how to extract these features accurately and efficiently is the key point. Based on this, this paper proposes a novel method for building contour extraction based on CSAR images. For strong scattering features, a fast fuzzy C-means (FCM) clustering algorithm was used to extract them. For anisotropic scattering features, aspect entropy was used to characterize the anisotropy degree, and K-means clustering was combined to extract. Finally, a more accurate result is obtained by merging the two feature extraction results. In order to verify the effectiveness and practicability of the proposed method, a lot of measured data acquired by the self-developed airborne L-band and Ku-band CSAR systems were processed. The experiments show that, compared with state-of-the-art algorithms, the proposed method can obtain more accurate results in less time.

Double-suppressed possibilistic fuzzy Gustafson–Kessel clustering algorithm

Possibilistic fuzzy c-means clustering (PFCM) is an unsupervised hybrid clustering algorithm, which can partly inherit the stability of fuzzy c-means clustering (FCM) algorithm and the noise robustness of possibilistic c-means clustering (PCM) algorithm. However, there are still several limitations in the PFCM when clustering complex data with multiple characteristics: sensitivity to strong noise, coincident clustering problem in multi-class clustering, and disability to deal with strong correlations of feature components. Therefore, a double-suppressed possibilistic fuzzy Gustafson–Kessel​ clustering algorithm (DS-PFGK) is presented. Firstly, we design a new objective function with a modified Mahalanobis distance and an improved weight parameter setting method based on the stability of fuzzy memberships and the anti-noise ability of possibilistic memberships. Moreover, combined with the absolute attribute of possibilistic memberships and the covariance matrix in the modified Mahalanobis distance, an ellipsoidal cluster core which can depict the distribution of sample data is generated to divide each cluster into different regions for selecting appropriate learning objects and identifying noisy data adaptively. Then, a “double-suppressed competitive learning” strategy is designed for the selected learning objects to solve the coincident clustering problem in multi-class clustering by suppressing the nonwinner possibilistic memberships, and to reduce the iteration number by suppressing nonwinner fuzzy memberships and rewarding winner fuzzy memberships. Finally, a segmentation algorithm on noisy color images based on the DS-PFGK is proposed based on the generated cluster cores. Experiments on synthetic datasets, real datasets, and color images show the good performance of the proposed methods compared with several classical and state-of-the-art clustering algorithms.

Thermographic image processing analysis in a solar concentrator with hard C-means clustering

Thermographic measurements are used to determine the temperatures reached by the focus of a modified Fresnel solar concentrator, where a container is placed to take advantage of this energy. The three steps of this investigation are: (i) the edges of each thermographic image are obtained by means of a Butterworth low-pass filter, (ii) the temperature grid in the solar concentrator is obtained by means of a feature extraction algorithm, and finally: (iii) the classification will be done through a C-means hard clustering algorithm selecting the center of each cluster to accurately find the temperature region to generate the isotherms and extract the temperatures with this algorithm. With the hard C-means algorithm, isotherm level curves and temperature graphs are obtained. Subsequently, two analyzes are carried out to validate that the original unprocessed thermographic images correspond spatially and in their spectrum with the processed images, with the aim of corroborating the acuteness of the digital image processing methodology implemented in this research. Finally, a correlation analysis is performed to validate the temperature matches of the original thermographic images.