FCM Clustering Algorithm Research Articles

An FCM clustering algorithm based on the identification of accounting statement whitewashing behavior in universities

Abstract The traditional recognition method of whitewash behavior of accounting statements needs to analyze a large number of special data samples. The learning rate of the algorithm is low, resulting in low recognition accuracy. To solve the aforementioned problems, this article proposes a method to identify the whitewash behavior of university accounting statements based on the FCM clustering algorithm. This article analyzes the motivation of university accounting statement whitewashing behavior, studies the common means of statement whitewashing, and establishes a fuzzy set for the identification of university accounting statement whitewashing behavior. By calculating the fuzzy partition coefficient, the membership matrix of whitewash behavior recognition is established, and the whitewash behavior is classified through the iteration of the FCM algorithm. The comparative experimental results show that the recognition method has good recognition performance, low recognition error rate, and recognition accuracy of 82%.

An improved FCM clustering algorithm with adaptive weights based on PSO-TVAC algorithm

An improved FCM clustering algorithm with adaptive weights based on PSO-TVAC algorithm

Application of Multiple Random Forest Algorithm in Image Segmentation of Nanoparticles

Because of its large specific surface area, small particle size, high surface energy, and unique nanoeffect, the morphological characteristics of nanoparticles are the key factors affecting the properties of materials. How to detect and evaluate the morphological characteristics of nanoparticles is the first problem to be solved in the preparation and application of nanomaterials. The main purpose of this paper is to use TEM to recognize the image features of nanoparticles and introduce the transmission electron microscope and image edge segmentation method and random forest algorithm. A method integrating the in situ characterization of modern electron microscopy and the measurement of the electrical properties of nanomonomers was developed. In this paper, a multielectrode TEM in situ electrical measurement platform is prepared, which improves the contact during the integration of nanomaterials and improves the electrical measurement accuracy of the TEM in situ electrical method. In this paper, based on the random forest algorithm, a multirandom forest algorithm is proposed. Due to the different gray levels of images referenced by the multirandom forest algorithm, the segmentation results are processed by FCM clustering algorithm. Experimental results show that in terms of image segmentation accuracy, the minimum Jaccard coefficient obtained by multiple random forest algorithm is 89% and 95%, respectively, which is obviously better than watershed segmentation method and maximum entropy threshold segmentation. In the aspect of automatic image segmentation of nanoparticles, the image segmentation accuracy is the highest when the sample block size and the number of sample blocks selected in the multiple random forest algorithm are 5∗5, 7500, and 35, respectively. Therefore, the multirandom forest algorithm has achieved high accuracy in image segmentation of nanoparticles, which provides valuable information for the preparation and application of nanomaterials. A new type of TEM dark‐field imaging diaphragm was prepared, which greatly improved the imaging quality of weak‐phase bulk materials represented by graphene and nonspiral biological samples represented by intracellular polyvesicles.

Feature-reduction Fuzzy c-means Clustering for Basketball Players Positioning

One of the best-known clustering methods is the fuzzy c-means clustering algorithm, besides k-means and hierarchical clustering. Since FCM treats all data features as equally important, it may obtain a poor clustering result. To solve the problem, feature selection with feature weighting is needed. Besides feature selection by assigning feature weights, there is also feature selection by assigning feature weights and eliminating the unrelated feature(s). THE Feature-reduction FCM (FRFCM) clustering algorithm can improve the FCM clustering result by weighting the features and discarding the unrelated feature(s) during the clustering process. Basketball is one of the famous sports, both international and national. There are five players in basketball, each with a different position. A player can generally be in guard, forward, or center position. Those three general positions need different characteristics of players’ physical conditions. In this paper, FRFCM is used to select the related physical feature(s) for basketball players, consisting of height, weight, age, and body mass index. to determine the basketball players’ position. The result shows that FRFCM can be applied to determine the basketball players’ position, where the most related physical feature is the player’s height. FRFCM gets one incorrect player’s position, so the error rate is 0.0435. As a comparison, FCM gets five incorrect player’s positions, with an error rate of 0.2174. This method can help the coach decide the basketball new player’s position.

The Bi-Level Optimal Configuration Model of the CCHP System Based on the Improved FCM Clustering Algorithm

To improve the comprehensive benefits of the CCHP system, this paper proposes a bi-level optimal configuration model of the CCHP system based on the improved FCM clustering algorithm. Firstly, based on the traditional FCM clustering algorithm, the entropy method is used to introduce the PFS index and the Vp index in a weighted form to achieve a comprehensive evaluation of the clustering effect. The effectiveness of the improved FCM algorithm is verified by analyzing the clustering process of the load and meteorological data using the improved FCM algorithm. Then the best cluster number and fuzzy coefficient is found using the traversal method. Secondly, a bi-level configuration optimization model is constructed. The outer layer is the configuration optimization layer, and the inner layer is the operation optimization layer. The model is solved by combining the NSGA-II and PSO algorithms. Finally, a bi-level optimal configuration model is constructed for actual cases, and the clustering results of the improved FCM algorithm are brought into the model. The example calculation analyses show that, compared with existing methods, the proposed method significantly reduces the operating cost and carbon dioxide emissions of the CCHP microgrid.

Robust copy-move forgery detection using modified superpixel based FCM clustering with emperor penguin optimization and block feature matching

Digital images face widespread tampering through a copy-move attack, wherein objects are added or removed from an image to create the copy-move forged image. This paper proposes a robust Copy Move Forgery Detection (CMFD) scheme by integrating both block-based and keypoint-based CMFD techniques. The input image is first segmented into non-overlapping blocks using a modified FCM clustering algorithm based on superpixels. The neighboring and similar superpixels influences are encompassed into FCM, and Emperor Penguin Optimization (EPO) is used to optimize the influential degree, which increases the segmentation performance. Gabor filter is applied to extract the features from each block. The Block features are then matched with each other to show the suspected forgery regions. The experimental results illustrate that the proposed technique attains better performance in terms of precision, recall, F1-score, and E-measure. It accomplishes excellent forgery detection outcomes compared with other existing CMFD processes, even under different perplexing circumstances.

Research on Accounting Intelligence System Modeling of Financial Performance Evaluation

In the current competitive market environment, both enterprises and academia attach great importance to the research of financial performance evaluation. The quality of financial performance directly affects the sustainable development of enterprises. With the deepening of enterprise management concept, enterprises pay more attention to the use of financial performance evaluation analysis to promote the sound development of the whole enterprise. In order to understand the development status and development trend of enterprises and improve the efficiency of enterprise management, it is of great significance to establish a scientific and professional financial performance evaluation model for the sustainable development of enterprises. In this paper, based on FCM clustering algorithm, a fuzzy decision model is established. Combined with the factors affecting the financial performance of enterprises, the corresponding indicators are selected from the four aspects of profitability, operation ability, debt paying ability, and development ability to construct the financial performance evaluation system, and the comprehensive fuzzy evaluation model is constructed according to the financial evaluation system. This paper selects listed companies as the research object to evaluate and analyze the performance of enterprises. The results show that the overall performance of listed companies is not ideal and solvency, operation ability, and development ability need to be enhanced. Finally, in view of the problems existing in the listed enterprises, this paper puts forward the following countermeasures: implement cost assessment and pay attention to cost analysis to strengthen cost control; strengthen the cooperation between industrial chain enterprises and R&D departments; and pay attention to the use of talents and improve the ability of independent innovation.

Optimal allocation of higher education resources based on fuzzy particle swarm optimization

The development concept of higher education will have a direct impact on the development direction of higher education, because the concept usually reflects people's understanding of the law of higher education, and the current education concept restricts the scale, quality, structure and efficiency of higher education to a certain extent. Throughout the evolution of the world's higher education development concept, it has generally experienced the overall coordination of the economic growth, the internal and external laws, and the people-centered sustainable development. This paper hopes to provide effective help by joint intelligent algorithms. In this paper, the particle swarm optimization algorithm in intelligent optimization algorithm and the FCM algorithm in cluster analysis are studied in depth, and the two are merged together to propose a fuzzy clustering algorithm based on improved particle swarm optimization algorithm. The main research contents of this thesis include: Firstly, the PSO algorithm is improved, and the random search step levy flights and PSO algorithm are repeatedly called by the iterative mode by introducing the eagle strategy. After improvement, the ESPSO algorithm can achieve a balance between global search and local development, and make the particles have more satisfactory search ability and convergence effect. Then the improved PSO algorithm is integrated into the FCM algorithm to solve the above problems of the FCM clustering algorithm and improve the clustering effect. The experimental results show that the improved espso algorithm and FCM algorithm are suitable for actual data. The simulation results show that the algorithm has better clustering effect than the traditional clustering algorithm.

基于PSO-TVAC的中心自适应权的FCM聚类算法

基于PSO-TVAC的中心自适应权的FCM聚类算法

Spinal cord segmentation and injury detection using a Crow Search-Rider optimization algorithm.

The damage in the spinal cord due to vertebral fractures may result in loss of sensation and muscle function either permanently or temporarily. The neurological condition of the patient can be improved only with the early detection and the treatment of the injury in the spinal cord. This paper proposes a spinal cord segmentation and injury detection system based on the proposed Crow search-Rider Optimization-based DCNN (CS-ROA DCNN) method, which can detect the injury in the spinal cord in an effective manner. Initially, the segmentation of the CT image of the spinal cord is performed using the adaptive thresholding method, followed by which the localization of the disc is performed using the Sparse FCM clustering algorithm (Sparse-FCM). The localized discs are subjected to a feature extraction process, where the features necessary for the classification process are extracted. The classification process is done using DCNN trained using the proposed CS-ROA, which is the integration of the Crow Search Algorithm (CSA) and Rider Optimization Algorithm (ROA). The experimentation is performed using the evaluation metrics, such as accuracy, sensitivity, and specificity. The proposed method achieved the high accuracy, sensitivity, and specificity of 0.874, 0.8961, and 0.8828, respectively that shows the effectiveness of the proposed CS-ROA DCNN method in spinal cord injury detection.

Different Approaches for Missing Data Handling in Fuzzy Clustering: A Review

Introduction: Incomplete data sets containing some missing attributes is a prevailing problem in many research areas. The reasons for the lack of missing attributes may be several; human error in tabulating/recording the data, machine failure, errors in data acquisition or refusal of a patient/customer to answer few questions in a questionnaire or survey. Further, clustering of such data sets becomes a challenge. Objective: In this paper, we presented a critical review of various methodologies proposed for handling missing data in clustering. The focus of this paper is the comparison of various imputation techniques based FCM clustering and the four clustering strategies proposed by Hathway and Bezdek. Methods: In this paper, we imputed the missing values in incomplete datasets by various imputation/ non-imputation techniques to complete the data set and then conventional fuzzy clustering algorithm is applied to get the clustering results. Results: Experiments on various synthetic data sets and real data sets from UCI repository are carried out. To evaluate the performance of the various imputation/ non-imputation based FCM clustering algorithm, several performance criteria and statistical tests are considered. Experimental results on various data sets show that the linear interpolation based FCM clustering performs significantly better than other imputation as well as non-imputation techniques. Conclusion: It is concluded that the clustering algorithm is data specific, no clustering technique can give good results on all data sets. It depends upon both the data type and the percentage of missing attributes in the dataset. Through this study, we have shown that the linear interpolation based FCM clustering algorithm can be used effectively for clustering of incomplete data set.

A Method of Operating State Estimation of Pumped Storage Power Station Based on Load Peak-Valley-Normal Prediction

In order to make the power system absorb more renewable energy and improve the flexibility of power grid operation, one of the effective methods is to configure pumped storage power station in power system. In order to reduce the frequent start-up and shutdown of pumped storage power station, “A Method of Operating State Estimation of Pumped Storage Power Station Based on Load Peak-Valley-Normal Prediction” is proposed in this paper. This method is based on FCM clustering algorithm classifies peak-valley-normal loads at one time of power grid, evaluates the operation state of pumped storage power station on this basis, and finally applies it to the operation cost optimization model of wind-solar-fire-storage system to evaluate the renewable energy absorption capacity and economic operation cost. The simulation results prove that this method can reduce the start-up and shutdown times of pumped storage power station and is of positive significance for maintaining long-term and stable operation of pumped storage power station.

A Method of Optimizing the Rule Base in the Sugeno Fuzzy Inference System Using Fuzzy Cluster Analysis

This paper develops a method of optimizing the rule base in the Sugeno-type fuzzy inference system using fuzzy cluster analysis for the design problems of control systems for complex technical objects. A hybrid control model with sequential interactions of classical and fuzzy controllers is used to validate the results. In the developed adaptive system of neuro-fuzzy inference, the rule base for the fuzzy controller is automatically formed based on the knowledge about the object obtained when it has been controlled by the classical controller, which excludes an expert's participation in the formation of the rule base. The optimization problem is solved using the method of grouping the values of the input and output signals in order to reduce the number of rules and to increase the speed of the control system of the technical object. The FCM clustering algorithm is used to group the values. As a result of the algorithm operation, not all of the set input actions arrive at the fuzzy controller input. Only cluster centers determined by fuzzy sets arrive, and the boundaries between clusters are also fuzzy. The effectiveness of the proposed grouping method for ensuring the effective control of a complex technical object under conditions of uncertainty is proved.

An Epilepsy Detection Method Using Multiview Clustering Algorithm and Deep Features.

The automatic detection of epilepsy is essentially the classification of EEG signals of seizures and nonseizures, and its purpose is to distinguish the different characteristics of seizure brain electrical signals and normal brain electrical signals. In order to improve the effect of automatic detection, this study proposes a new classification method based on unsupervised multiview clustering results. In addition, considering the high-dimensional characteristics of the original data samples, a deep convolutional neural network (DCNN) is introduced to extract the sample features to obtain deep features. The deep feature reduces the sample dimension and increases the sample separability. The main steps of our proposed novel EEG detection method contain the following three steps: first, a multiview FCM clustering algorithm is introduced, and the training samples are used to train the center and weight of each view. Then, the class center and weight of each view obtained by training are used to calculate the view-weighted membership value of the new prediction sample. Finally, the classification label of the new prediction sample is obtained. Experimental results show that the proposed method can effectively detect seizures.

Predicting Crash Injury Severity with Machine Learning Algorithm Synergized with Clustering Technique: A Promising Protocol.

Predicting crash injury severity is a crucial constituent of reducing the consequences of traffic crashes. This study developed machine learning (ML) models to predict crash injury severity using 15 crash-related parameters. Separate ML models for each cluster were obtained using fuzzy c-means, which enhanced the predicting capability. Finally, four ML models were developed: feed-forward neural networks (FNN), support vector machine (SVM), fuzzy C-means clustering based feed-forward neural network (FNN-FCM), and fuzzy c-means based support vector machine (SVM-FCM). Features that were easily identified with little investigation on crash sites were used as an input so that the trauma center can predict the crash severity level based on the initial information provided from the crash site and prepare accordingly for the treatment of the victims. The input parameters mainly include vehicle attributes and road condition attributes. This study used the crash database of Great Britain for the years 2011–2016. A random sample of crashes representing each year was used considering the same share of severe and non-severe crashes. The models were compared based on injury severity prediction accuracy, sensitivity, precision, and harmonic mean of sensitivity and precision (i.e., F1 score). The SVM-FCM model outperformed the other developed models in terms of accuracy and F1 score in predicting the injury severity level of severe and non-severe crashes. This study concluded that the FCM clustering algorithm enhanced the prediction power of FNN and SVM models.

Brain Tumor Segmentation and identification based on Image Processing FCM Clustering Algorithm

Brain Tumor Segmentation and identification based on Image Processing FCM Clustering Algorithm

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

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

Infrared image-based detection method of electrical equipment overheating area in substation

For the detection of overheated areas of electrical equipment, in order to accurately segment out the overheated areas and reduce the fault detection range, this paper proposes a new overheated area detection algorithm. Firstly, the Ostu algorithm is used to remove the background and segment the general area of the electrical equipment area; secondly, the active contour model is used to refine the edge of the target area to remove the redundant edge points; finally, FCM clustering algorithm is used to suppress over segmentation and accurately divide the overheated area. The experiment proves that the algorithm can accurately divide the overheated area, and has certain practical value.

Automatic measurement of traditional Chinese costume from its silhouette through Fuzzy c-means clustering method

As the precious cultural heritage of the Chinese nation, traditional costumes are in urgent need of scientific research and protection. In particular, there are scanty studies on costume silhouettes, due to the reasons of the need for cultural relic protection, and the strong subjectivity of manual measurement, which limit the accuracy of quantitative research. This paper presents an automatic measurement method for traditional Chinese costume dimensions based on fuzzy C-means clustering and silhouette feature point location. The method is consisted of six steps: (1) costume image acquisition; (2) costume image preprocessing; (3) color space transformation; (4) object clustering segmentation; (5) costume silhouette feature point location; and (6) costume measurement. First, the relative total variation model was used to obtain the environmental robustness and costume color adaptability. Second, the FCM clustering algorithm was used to implement image segmentation to extract the outer silhouette of the costume. Finally, automatic measurement of costume silhouette was achieved by locating its feature points. The experimental results demonstrated that the proposed method could effectively segment the outer silhouette of a costume image and locate the feature points of the silhouette. The measurement accuracy could meet the requirements of industrial application, thus providing the dual value of costume culture research and industrial application.

A Wind Farm Equivalent Method Based on Multi-View Transfer Clustering and Stack Sparse Auto Encoder

Large-scale wind farm (WF) constitutes dozens or even hundreds of wind turbines (WTs), making it complex and even impractical to develop each individual WT in detail when building WF model. Thus, the equivalent model of WF, with a reasonable reduction of the detailed model, is essential to be developed. In this paper, we propose a multi-view transfer clustering and stack sparse auto encoder (SSAE) based WF equivalent method, which can be used in the low voltage ride through (LVRT) analysis of WF. First, to obtain distinguishable deep-level and multi-view representation of wind turbine (WT), stack sparse auto encoder (SSAE) is used to extract features from the time series of several WT physical quantities, and these features are used as the clustering indicator (CI). Then, a multi-view transfer FCM (MVT-FCM) clustering algorithm, which combines transfer learning with multi-view FCM (MV-FCM), is put forward for WTs clustering. Two transfer rules are designed in this algorithm, and the clustering center and membership degree in the source domain are transferred to guide the clustering process of target domain samples. Finally, the calculation method of equivalent parameters is presented. To verify the effectiveness of the proposed method, a modified actual system in East Inner Mongolia of China is utilized for case study, and the performance of the proposed model is compared with several state-of-the-art models. Simulation results show that the equivalent errors of the proposed model decrease at least 3% when comparing with other models. Also, the error fluctuations are within 6% under different simulation conditions, which illustrates the well-performed robustness of the proposed model.