C-means Clustering Algorithm Research Articles

An improved fuzzy C-means clustering algorithm using Euclidean distance function

The fuzzy c-mean (FCM) clustering algorithm is a typical algorithm using Euclidean distance for data clustering and it is also one of the most popular fuzzy clustering algorithms. However, FCM does not perform well in noisy environments due to its possible constraints. To improve the clustering accuracy of item varieties, an improved fuzzy c-mean (IFCM) clustering algorithm is proposed in this paper. IFCM uses the Euclidean distance function as a new distance measure which can give small weights to noisy data and large weights to compact data. FCM, possibilistic C-means (PCM) clustering, possibilistic fuzzy C-means (PFCM) clustering and IFCM are run to compare their clustering effects on several data samples. The clustering accuracies of IFCM in five datasets IRIS, IRIS3D, IRIS2D, Wine, Meat and Apple achieve 92.7%, 92.0%, 90.7%, 81.5%, 94.2% and 88.0% respectively, which are the highest among the four algorithms. The final simulation results show that IFCM has better robustness, higher clustering accuracy and better clustering centers, and it can successfully cluster item varieties.

Dynamic conditional score model-based weighted incremental fuzzy clustering of consumer power load data

This study proposes a weighted incremental fuzzy C-mean power load clustering algorithm based on the dynamic conditional score model to solve the problems that the predominant power load data mining method only captures the mean characteristics of time series and ignores the heteroscedasticity and sequence correlations. Consequently, this method has unsatisfactory time series clustering and low clustering accuracy. A dynamic conditional score model is constructed to analyze and extract statistical characteristic parameters of a time series to calculate the autocorrelation value of the parameter series. A weighted fuzzy C-mean clustering analysis is performed, and the obtained data weight information is used as input for incremental clustering to improve the clustering accuracy. The DCS model parameter dataset and data weight information are combined, and the clustering analysis of the consumer power load data stream is performed. The power load time series of different companies is given, and the clustering validity indices are defined for the performance analysis to verify the proposed clustering algorithm. The experimental results show that the proposed algorithm achieves satisfactory clustering and improves the performance.

Medical Image Analysis of Lung Tumor Diagnosis Based on Generalized Fuzzy C-Means Clustering Algorithm

In recent years, the rapid development of medical imaging technology has brought medical image analysis into the era of big data. CT imaging technology is one of the most common imaging methods for disease screening. This paper introduces medical image analysis of lung tumor diagnosis based on Generalized fuzzy c - means clustering algorithm was proposed. Brief introduce lung CT imaging to diagnose lung tumors requires a huge workload, which is often accompanied by long-term image reading and subjective evaluation. The experimental data comes from 15 patient samples which include two groups of samples are selected as test objects. The result shows the positive rate is high, resulting in misdiagnosis. In addition, great success of deep learning technology in the field of computer vision has made it possible to realize computer-aided diagnosis and screening of lung cancer.

Hard c-mean transition network method for analysis of time series.

Transition network is a powerful tool to analyze nonlinear dynamic characteristics of complex systems, which characterizes the temporal transition property. Few, if any, existing approaches map different time series into transition networks with the same size so that temporal information of time series can be captured more effectively by network measures including typical average node degree, average path length, and so on. To construct a fixed size transition network, the proposed approach uses the embedding dimension method to reconstruct phase space from time series and divides state vectors into different nodes based on the hard c-mean clustering algorithm. The links are determined by the temporal succession of nodes. Our novel method is illustrated by three case studies: distinction of different dynamic behaviors, detection of parameter perturbation of dynamical system, and identification of seismic airgun based on sound data recorded in central Atlantic Ocean. The results show that our proposed method shows good performance in capturing the underlying nonlinear and nonstationary dynamics from short and noisy time series.

Individual tree-based forest species diversity estimation by classification and clustering methods using UAV data

Monitoring forest species diversity is essential for biodiversity conservation and ecological management. Currently, unmanned aerial vehicle (UAV) remote sensing technology has been increasingly used in biodiversity monitoring due to its flexibility and low cost. In this study, we compared two methods for estimating forest species diversity indices, namely the spectral angle mapper (SAM) classification approach based on the established species-spectral library, and the self-adaptive Fuzzy C-Means (FCM) clustering algorithm by selected biochemical and structural features. We conducted this study in two complex subtropical forest areas, Mazongling (MZL) and Gonggashan (GGS) National Nature Forest Reserves using UAV-borne hyperspectral and LiDAR data. The results showed that the classification method performed better with higher values of R2 than the clustering algorithm for predicting both species richness (0.62 > 0.46 for MZL and 0.55 > 0.46 for GGS) and Shannon-Wiener index (0.64 > 0.58 for MZL, 0.52 > 0.47 for GGS). However, the Simpson index estimated by the classification method correlated less with the field measurements than the clustering algorithm (R2 = 0.44 and 0.83 for MZL and R2 = 0.44 and 0.62 for GGS). Our study demonstrated that the classification method could provide more accurate monitoring of forest diversity indices but requires spectral information of all dominant tree species at individual canopy scale. By comparison, the clustering method might introduce uncertainties due to the amounts of biochemical and structural inputs derived from the hyperspectral and LiDAR data, but it could acquire forest diversity patterns rapidly without distinguishing the specific tree species. Our findings underlined the advantages of UAV remote sensing for monitoring the species diversity in complex forest ecosystems and discussed the applicability of classification and clustering methods for estimating different individual tree-based species diversity indices.

Analysis of the Improvement of Engineering Mechanics Experimental Methods Based on IoT and Machine Learning

With the rapid development of sensor technology, machine learning, and the Internet of Things, wireless sensor networks have gradually become a research hotspot. In order to improve the data fusion performance of wireless sensor networks and ensure network security in the event of external attacks, this paper proposes a wireless sensor optimization algorithm model, involving wireless sensor networks, the Internet of Things, and other related fields. This paper first analyzes the role of the Internet of Things in wireless sensor networks, studies the localization mechanism and hierarchy of the Internet of Things based on wireless sensor networks, and improves the LE-RLPCCA (Position Estimation Robust Local Retention Criteria Correlation Analysis) localization algorithm model based on sensor grids. This paper discusses the problems of machine learning in wireless sensor networks, constructs a sensor-based machine learning model, and designs a data fusion algorithm for a wireless sensor networks' machine learning model. The application of wireless sensors in engineering mechanics experiments is summarized, and the optimization algorithm model of the wireless sensor in engineering mechanics experiments is proposed. The analysis results show that the average accuracy of the DKFCM-FSVM (Density aware Kernel-based Fuzzy C-means Clustering algorithm Fuzzy Support Vector Machine) algorithm in detecting five behaviors is 0.997, 0.992, 0.904, 0.996, and 0.946, respectively, and the accuracy in detecting different behaviors is the best, 0.005, 0.01, 0.003, and 0.006 respectively. It achieves the lowest false positive rate in the detection of different behaviors, and the average false positive rate is 0.004, 0.003, 0.003, 0.008, and 0.005, respectively, which shows that the DKFCM-FSVM algorithm model of wireless sensor networks in engineering mechanics experiments is the optimal solution. The work of this paper has good reference value for the application of wireless sensor networks and the optimization of engineering mechanics experimental methods and is helpful for further research of sensor technology.

Patch-Based Fuzzy Local Weighted C-Means Clustering Algorithm with Correntropy Induced Metric for Noise Image Segmentation

Patch-Based Fuzzy Local Weighted C-Means Clustering Algorithm with Correntropy Induced Metric for Noise Image Segmentation

A proposed scenario to improve the Ncut algorithm in segmentation.

In image segmentation, there are many methods to accomplish the result of segmenting an image into k clusters. However, the number of clusters k is always defined before running the process. It is defined by some observation or knowledge based on the application. In this paper, we propose a new scenario in order to define the value k clusters automatically using histogram information. This scenario is applied to Ncut algorithm and speeds up the running time by using CUDA language to parallel computing in GPU. The Ncut is improved in four steps: determination of number of clusters in segmentation, computing the similarity matrix W, computing the similarity matrix's eigenvalues, and grouping on the Fuzzy C-Means (FCM) clustering algorithm. Some experimental results are shown to prove that our scenario is 20 times faster than the Ncut algorithm while keeping the same accuracy.

Segmentation of Brain Tissues from MRI Images Using Multitask Fuzzy Clustering Algorithm.

In recent years, brain magnetic resonance imaging (MRI) image segmentation has drawn considerable attention. MRI image segmentation result provides a basis for medical diagnosis. The segmentation result influences the clinical treatment directly. Nevertheless, MRI images have shortcomings such as noise and the inhomogeneity of grayscale. The performance of traditional segmentation algorithms still needs further improvement. In this paper, we propose a novel brain MRI image segmentation algorithm based on fuzzy C-means (FCM) clustering algorithm to improve the segmentation accuracy. First, we introduce multitask learning strategy into FCM to extract public information among different segmentation tasks. It combines the advantages of the two algorithms. The algorithm enables to utilize both public information among different tasks and individual information within tasks. Then, we design an adaptive task weight learning mechanism, and a weighted multitask fuzzy C-means (WMT-FCM) clustering algorithm is proposed. Under the adaptive task weight learning mechanism, each task obtains the optimal weight and achieves better clustering performance. Simulated MRI images from McConnell BrainWeb have been used to evaluate the proposed algorithm. Experimental results demonstrate that the proposed method provides more accurate and stable segmentation results than its competitors on the MRI images with various noise and intensity inhomogeneity.

Detection and comparison of Diabetic Maculopathy using C-Means Clustering Algorithm and Watershed Algorithm

Aim: The aim of this research work is for the presence of Novel Diabetic Maculopathy Detection using modern algorithms, and comparing the Peak Signal to Noise Ratio (PSNR) between the C-Means clustering Algorithms and Watershed Algorithm. Materials and Methods: The sample images were taken from kaggle’s website. Samples were considered as (N=24) for C-Means Clustering Algorithm and (N=24) for Watershed algorithm in accordance with total sample size calculated using clinicalc.com by keeping alpha error-threshold value 0.05, enrollment ratio as 0.1, 95% confidence interval, G power as 80%. The Peak Signal to Noise Ratio was calculated by using the MATLAB Programming with a standard data set. Results: Comparison of PSNR is done by independent sample t-test using SPSS software. There is a statistical insignificant difference between C-Means Clustering Algorithm and Watershed algorithm with p=0.11, p>0.05 (PSNR = 35.3411) showed better results in comparison to Watershed Algorithm (PSNR =9.7420). Conclusion: C-Means Clustering Algorithms were found to give higher PSNR than in Watershed Algorithms for the Novel Diabetic Maculopathy Detection.

Sparse FCM-Based Map-Reduce Framework for Distributed Parallel Data Clustering in E-Khool Learning Platform

Parallel clustering serves as a platform for handling big data. The literature displays a number of clustering algorithms using a map-reduce framework, but they did not assure the effective clusters such that knowledge extraction becomes tough. With the aim to render a better and effective data clustering method to analyze the big data arriving from distributed systems, this paper uses a new clustering method. The proposed method named as BatDolphin-based Sparse Fuzzy C-Means (BatDol-Sparse FCM) clustering algorithm is proposed that paves way for the optimal selection of the cluster centroids. The distributed big data is managed using the Map-Reduce framework that is inbuilt with the BatDolphin-based Sparse Fuzzy C-Means algorithm such that the local and global clustering is executed. The implementation of the proposed BatDol-Sparse FCM algorithm is done by using data from the E-khool Learning Management System (LMS) and three medical datasets from the UCI repository. The analysis is performed using the metrics, such as clustering accuracy (CA) and dice coefficient (DC). From the simulation results, it is evident that the proposed parallel clustering scheme provided better results than the existing algorithms with the values of 0.96 and 0.9667 for CA and DC respectively.

Equivalent modeling and multi-parameter coupling optimization for DFIG-based wind farms considering SSO mode

As a low-carbon and environmentally friendly renewable energy source, wind power has been globally recognized as the best solution to achieve energy saving and emission reduction and promote low-carbon economic growth. With the increase of wind power penetration, wind power has a great impact on sub-synchronous state stability and dynamic characteristics of the grid-connected system. Aiming at the fact that the correlation between clustering indexes and sub-synchronous oscillation (SSO) mode and the difference of the contribution to the clustering results are seldom considered in the current equivalent modeling of doubly-fed induction generator (DFIG)-based wind farm, this paper proposes a clustering method based on the index dimension reduction and weighted fuzzy C-means (WFCM) clustering algorithm. Besides, for the SSO study of the grid-connected system without sufficiently considering the coupling effects between controller parameters, a multi-parameter coupling optimization design strategy combining orthogonal experiment method (OEM) and response surface method is proposed. Firstly, the dominant variables of SSO mode of the DFIG-based wind farm connected to weak grid by series compensation system are taken as the initial clustering indexes. After dimension reduction by principal component analysis, the WFCM algorithm is utilized to cluster the wind farm. Then, the proportional and integral coefficients of the grid-side controller, rotor-side controller and phase-locked loop are optimized to achieve the simultaneous optimization of the SSO characteristics and dynamic characteristics of the system. Finally, the interaction between control parameters and the influence degree and trend on the system performance are quantitatively evaluated, and the optimal parameter combination is obtained. The proposed strategy can mitigate SSO more effectively while improving anti-interference than the particle swarm optimization based on OEM.

Survey on Relational Database Watermarking Employing Evolutionary Methods

Ownership control, integrity, and tamper-proofing of relational data are essential concerns that must be addressed as the communication (network) system grows. Over the past several years, a plethora of potential solutions has been proposed to address these issues (including cryptography, steganography, digital signatures, watermarks, and many others). Digital watermarking (comparatively new) is a technology that aids in the resolution of issues such as data theft, authenticity, and copyright claims. This paper’s primary contributions are as follows: 1) Examine evolutionary algorithm-based watermarking in relational databases that provide optimize data encoding space in search space which provides high robustness and imperceptibility. 2) Examine contemporary database watermarking strategies based on evolutionary algorithms for high embedding capacity and watermark bit insertion. 3) Malicious agent tracing through cluster-based (mainly fuzzy c-mean clustering algorithm or based on a hash function) or multiple watermarking approaches over shared or collaborative networks. This article focuses on watermarking numeric relational databases for authentication and integrity. Also provide a brief overview of the development of relational database watermarking and emergence, its characteristics and application, and the popular research methods currently in use. In addition, gist on various possible attacks. At last, this paper suggests directions for further research in these areas. Researchers can use the findings of this study to build secure watermarking methods for databases.

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.

Ensemble of Effect Size Methods Based on Meta Fuzzy Functions

Many methods are used in the literature to determine the effect size (ES) for two independent groups. Many of these methods yield consistent results under the assumptions of normality of data and homogeneity of variances. However, not every dataset can provide these assumptions. In order to overcome the limitations mentioned, this study proposes the use of meta fuzzy effect size functions (MFESF). The MFESF weights six ES methods (Cohen’s d, Hedge’s g, and Glass’ delta, Cliff’s delta, Vargha and Delaney A and Glass’ rank-biserial correlation) used for two independent groups according to their performances and provides better outcomes, regardless of assumptions. The MFESF method uses the fuzzy c-means (FCM) clustering algorithm to combine the selected ES methods. In this study, MFESF is evaluated by using generated datasets based on normal and non-normal distribution for six reference values. In addition, the performance of MFESF is evaluated by using real datasets with normal and non-normal distributions. As a result, the MFESF performed the best with the lowest mean absolute percentage error (MAPE) compared to the individual ES methods for all datasets.

Risk Evaluation of Logistics Park Projects’ Lifecycle during the COVID-19 Pandemic: Failure Mode and Effects Analysis

Logistics parks served as critical logistics facilities during the coronavirus disease 2019 pandemic. Constructing these large-scale projects including their subsequent operations involved unusual risk factors that required comprehensive risk evaluations. Therefore, it is vital to examine the potential risk factors across the entire life cycle of these projects in accordance with the complexity of these large-scale logistics park projects (LLPPs) during the pandemic. This study proposes a new risk analysis model that evaluates a comprehensive list of risk factors based on the failure mode and effects analysis (FMEA). The hesitant uncertain linguistic Z-numbers (HULZNs) and fuzzy C-means (FCM) clustering algorithm are incorporated into the FMEA model to evaluate the risk. The practicability of the model was assessed in a case study. The findings identified and distinguished risk factors based on their levels of influences and priorities in dealing with uncertainties during the decision-making process in LLPPs. The FMEA model’s new methodological assessment also contributes to the efficient risk evaluation process through its more reliable and reasonable failure mode risk clustering, particularly for complex group decision-making environments during the pandemic.

Neural network approach to detect renal calculi

Back Propagation Network is the most commonly used algorithm in training neural networks. It is employed in processing the images and data to implement an automated kidney stone classification. The conventional technique for classifying medical resonance kidney images and detecting stones relies on human examination. This method is not accurate since it is impractical to handle large amount of data. Magnetic Resonance (MR) Images may inherently possess noise caused by operator errors. This causes earnest inaccuracies in classification features and diseases in image processing. However, the usage of artificial intelligent based methods along with neural networks and feature extraction has shown great potential in extracting the region of interest using back propagation network algorithms in this field. In this work, the Back Propagation Network was applied for the objective of kidney stone detection. Decision-making is carried out in two stages, Feature extraction and Image classification. The feature extraction is done using the principal component analysis and the image classification is done using Back Propagation Network (BPN). This work presents a segmentation method using the Fuzzy C-Mean (FCM) clustering algorithm. The performance of the BPN classifier was estimated in terms of training execution and classification accuracy. The Back Propagation Network gives precise classification when compared to other methods based on neural networks.

Retracted: Deep Possibilistic C-means Clustering Algorithm on Medical Datasets.

[This retracts the article DOI: 10.1155/2022/3469979.].

Feature Extraction and Pattern Recognition Algorithm of Power Cable Partial Discharge Signal

The degree of insulation aging of power cables is closely related to their partial discharge (PD) level, so the analysis of PD signals can be used to realize the cable condition detection. However, after performing online detection of PDs on power cables, the collected signals always contain interference signals due to the influence of electromagnetic interference in the field. In order to identify each type of local discharge signal from the interference signal, this paper proposes a clustering identification algorithm for local discharge signals, which mainly involves pulse extraction, feature parameter extraction and clustering identification process. The algorithm first extracts the pulse signal by combining the amplitude–time threshold method and the time domain energy method, then obtains the feature vector of the signal according to the synchronous multi-channel method, designs a fuzzy C-mean clustering algorithm based on subtractive clustering to determine the initial clustering center to cluster the samples and finally analyzes and checks the clustering results according to the phase resolved PD (PRPD) of a single class of signals and the fit of the two-parameter Weibull distribution function. The clustering results were analyzed and examined. The experimental results show that the proposed algorithm can extract pulse signals efficiently and accurately, and the synchronous multi-channel method can characterize pulse signals better. Meanwhile, the algorithm can determine the optimal number of classes adaptively according to the clustering effectiveness function and adopt subtractive clustering to initialize the clustering center, which can approach the optimal solution faster, and can effectively cluster a variety of discharge signals, so as to realize the type identification of single-class discharge signals.

A novel approach to fuzzy c-Means clustering using kernel function

Clustering is an unsupervised procedure that divides a set of objects into homogeneous groups. Two types of clustering are possible, Hard clustering and Soft clustering/Fuzzy clustering. Hard clustering is not feasible for complex datasets that contain uncertainty and overlapping clusters, whereas fuzzy clustering efficiently handles it. FCM is sensitive to the initial values and challenging to cluster nonlinear data. A new approach is implemented here with the Fuzzy c-Means (FCM) clustering algorithm to improve the performance. The Kernel function ensures the linear separability of complex clusters by projecting the feature space into a higher dimension and not subject to the initial values. The Kernel-based FCM (KFCM) optimized the clustering. The relevant features are considered for clustering, and it improves the validity of clusters. The irrelevant features blur the clusters and reduce the quality. Silhouette index (SI) and Davies-Bouldin index (DBI) have been used as the evaluation function. The experiments are conducted on two benchmark datasets and one artificial dataset. The result justifies Kernel-based FCM, and the superiority of features reduced Kernel-based FCM clustering over other traditional fuzzy clustering techniques.