Fuzzy Clustering Technique Research Articles

Identifying at-risk patients for congenital heart disease using integrated predictive models and fuzzy clustering analysis: A cross-sectional study

Congenital heart disease (CHD) remains a significant global health concern, affecting approximately 1 % of newborns worldwide. While its accurate causes often remain elusive, a combination of genetic and environmental factors is implicated. In this cross-sectional study, we propose a comprehensive prediction framework leveraging Machine Learning (ML) and Multi-Attribute Decision Making (MADM) techniques to enhance CHD diagnostics and forecasting. Our framework integrates supervised and unsupervised learning methodologies to remove data noise and address imbalanced datasets effectively. Through the utilization of imbalance ensemble methods and clustering algorithms such as K-means, we enhance predictive accuracy, particularly in non-clinical datasets where imbalances are prevalent. Our results demonstrate an improvement of 8 % in recall compared to existing literature, showcasing the efficacy of our approach. Moreover, our framework identifies clusters of patients at the highest risk using MADM techniques, providing insights into susceptibility to CHD. Fuzzy clustering techniques further assess the degree of risk for individuals within each cluster, enabling personalized risk evaluation. Importantly, our analysis reveals that unhealthy lifestyle factors, annual per capita income, nutrition, and folic acid supplementation emerge as crucial predictors of CHD occurrences. Additionally, environmental risk factors and maternal illnesses significantly contribute to the predictive model. These findings underscore the multifactorial nature of CHD development, emphasizing the importance of considering socioeconomic and lifestyle factors alongside medical variables in CHD risk assessment and prevention strategies. Our proposed framework offers a promising avenue for early identification and intervention, potentially mitigating the burden of CHD on affected individuals and healthcare systems globally.

A Novel Approach for Detection, Segmentation, and Classification of Brain Tumors in MRI Images Using Neural Network and Special C Means Fuzzy Clustering Techniques

Brain tumors present a significant health challenge globally, necessitating advanced techniques for accurate detection, segmentation, and classification. This paper presents a comprehensive study focused on the development and evaluation of innovative methodologies for brain tumor analysis using medical imaging data. The primary objective of this research is to enhance the accuracy and efficiency of brain tumor detection, segmentation, and classification processes. To achieve this goal, a multi-step approach is proposed, integrating various computational techniques and machine learning algorithms. First, the study explores novel methods for preprocessing medical imaging data to enhance image quality and reduce noise artifacts. This preprocessing step plays a crucial role in improving the subsequent analysis stages' accuracy and reliability. Next, a robust tumor detection algorithm is developed, leveraging advanced image processing techniques and deep learning models. The proposed algorithm effectively identifies tumor regions within brain images with high accuracy and minimal false positives. Following tumor detection, a segmentation framework is introduced to precisely delineate tumor boundaries from surrounding healthy tissues. The segmentation algorithm combines traditional image processing methods with state-of-the-art deep learning architectures to achieve accurate and efficient tumor delineation. In this paper, we proposed three novel methods for automatic detection, classification, and segmentation of brain tumors. Comprehensive experiments are conducted on the BRATS dataset and show that the proposed model obtains competitive results. The parameters under study are Accuracy rate, Specificity, and Sensitivity. First approach focuses on classifying cancerous and non-cancerous brain tumors in MRI scans. The system first reads the images and employs a novel fusion method to combine information from various modalities (Flair, T1, T1C, T2) for a more comprehensive picture. This enhances the accuracy of tumor characterization. Following this fusion, the images undergo preprocessing, feature extraction, and classification. The preprocessing stage involves grayscale conversion, binarization, wavelet analysis, and region-of-interest (ROI) calculation. Finally, a robust Neural Network (NN) classification method effectively differentiates cancerous and non-cancerous brain tissue. Performance is evaluated by metrics like accuracy, sensitivity, and specificity. Compared to existing methods, this research system demonstrates superior results, achieving a classification accuracy of 96.61%, sensitivity of 96.66%, and specificity of 96.55%. The Second approach combines Backpropagation Neural Networks (BPNN) with Spatial Fuzzy C-Means (SFCM) clustering for brain tumor analysis. The BPNN classifies brains into normal, benign (non-cancerous abnormality), or malignant (cancerous) categories. SFCM helps pinpoint the exact tumor location and size within the MRI scan through segmentation. A dual-tree complex wavelet transform is employed to extract image features efficiently. This method achieved exceptional results: 99.77% classification accuracy, 99.87% sensitivity (correctly identifying tumors), and 98.69% specificity (correctly identifying healthy tissue). Additionally, the over 90% precision demonstrates the effectiveness of this technique in feature extraction and classification. The experiments demonstrate that BPNN-SFCM successfully segment, and extracts brain tumors from MRI scans. The overall accuracy of BPNN-SFCM is better as compared to other proposed methods and existing methods.

Pattern Identification Using Fuzzy Cluster Analysis and Latent Class Analysis: A Case Study in Perú

The Demographic and Family Health Survey (ENDES) conducted by the National Institute of Statistics and Informatics (INEI) in Peru provides data on fertility and health. The ENDES 2020 report, based on 35,847 surveyed households, undergoes descriptive statistical analysis with the aim of identifying patterns to enhance social conditions. Techniques such as Fuzzy C-Means and Latent Classes, previously applied in various contexts, are employed. Correlation analysis using the R polycor package highlights significant relationships, leading to the exclusion of certain numeric variables in fuzzy clustering due to strong correlations. Random sampling is applied to address the data volume. Three clusters are determined through kmeans clustering, silhouette, Elbow, and Clara methods, assessing their fuzziness with the Dunn's Fuzziness Coefficient. Pattern identification reveals significant differences in family relationships, gender, education, and health insurance among the clusters. The widespread lack of health insurance, particularly ESSALUD/IPSS, stands out as a common issue. Fuzzy clustering and latent class analysis techniques provide groupings with variations in sizes and compositions. Received: 20 March 2024 / Accepted: 28 June 2024 / Published: 02 July 2024

PCA and fuzzy clustering-based delineation of soil nutrient (S, B, Zn, Mn, Fe, and Cu) management zones of sub-tropical Northeastern India for precision nutrient management

Understanding the spatial distribution of plant available soil nutrients and influencing soil properties and delineation soil nutrient management zones (MZs) are important for implementing precision nutrient management options (PNMO) in an area to achieve maintainable crop production. We assessed spatial distribution pattern of plant available sulphur (S) (PAS), boron (B) (PAB), zinc (PAZn), manganese (PAMn), iron (PAFe), and copper (PACu), and soil organic carbon (SOC), pH, and electrical conductivity (EC) to delineate soil nutrients MZs in northeastern region of India. A total of 17,471 representative surface (0–15 cm depth) soil samples were collected from the region, processed, and analysed for above-mentioned soil parameters. The values of PAS (0.22–99.2 mg kg−1), PAB (0.01–6.45 mg kg−1), PAZn (0.05–13.9 mg kg−1), PAMn (0.08–158 mg kg−1), PAFe (0.50–472 mg kg−1), PACu (0.01–19.2 mg kg−1), SOC (0.01–5.80%), pH (3.19–7.56) and EC (0.01–1.66 dS m−1) varied widely with coefficient of variation of 15.5–108%. The semivariogram analysis highlighted exponential, Gaussian and stable best fitted models for soil parameters with weak (PACu), moderate (PAB, PAZn, PAFe, SOC, pH, and EC) and strong (PAS, and PAMn) spatial dependence. The ordinary kriging interpolation revealed different distribution patterns of soil parameters. About 14.8, 27.5, and 3.40% area of the region had PAS of ≤15.0 mg kg−1, PAB of ≤0.50 mg kg−1, and PAZn of had ≤0.90 mg kg−1, respectively. About 67.5, and 32.5% area had SOC content >1.00 and < 1.00%, respectively. Soil pH was ≤5.50, and >5.50 to ≤6.50 in 41.7 and 40.3% area of the region, respectively. The techniques of principal component analysis and fuzzy c-mean algorithm clustering produced 6 MZs of the region with different areas and values of soil parameters. The MZs had different levels of deficiency pertaining to PAS, PAB, and PAZn. The produced MZ maps could be used for managing PAS, PAB, PAZn, SOC and soil pH in order to implement PNMO. The study highlighted the usefulness of MZ delineation technique for implementation of PNMO in different cultivated areas for sustainable crop production.

Efficient fuzzy methodology for congestion control in wireless sensor networks

In wireless sensor networks (WSNs), ensuring reliable and efficient communication between nodes requires effective congestion control. This study presents an innovative approach, called FSFG, which combines FCM fuzzy clustering, Fuzzy Inference System (FIS), Support Vector Regression (SVR), and Genetic Algorithm (GA) to address congestion in WSNs. FSFG consists of two phases: Structure Identification and Parameter Identification. In the first phase, fuzzy clustering techniques are employed to determine the desirability of outputs in different classes. Subsequently, support vector regressions are used to determine membership functions with a minimal error rate. In the parameter identification phase, the obtained rules from the FCM method are used to design the desired system, enabling the estimation of necessary outputs based on relevant inputs. Through simulations, the results demonstrate that the FSFG algorithm excels in congestion control, exhibiting a lower error rate and outperforming other algorithms in terms of delay and packet delivery rate.

Brain image segmentation with fuzzy entropy clustering and PSO-GWO optimization techniques

In the field of brain MRI analysis, image segmentation serves various purposes such as quantifying and visualizing anatomical structures, analyzing brain changes, delineating pathological regions, and aiding in surgical planning and image-guided interventions. Over the past few decades, diverse segmentation techniques with varying degrees of accuracy and complexity have been developed. Real-world brain MRI images often encounter intensity in homogeneity, posing a significant challenge in accurate segmentation. The prevailing image segmentation algorithms, predominantly region-based, typically rely on the homogeneity of image intensities in specific regions of interest. However, these methods often fall short of providing precise segmentation results due to intensity in homogeneity. To address these challenges and enhance segmentation performance, this paper introduce a novel objective function named Fuzzy Entropy Clustering with Local Spatial Information and Bias Correction (FECSB). Additionally, we propose a novel hybrid algorithm that combines Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) to maximize the effectiveness of the FECSB function in MRI brain image segmentation. The proposed algorithm undergoes rigorous evaluation using benchmark MRI brain images, including those from the McConnell Brain Imaging Center (BrainWeb). The experimental results unequivocally demonstrate the superiority of the PSO-GWO clustering method over the traditional Fuzzy C Means (FCM) method. Across various image slices, the PSO-GWO method consistently outperforms FCM in terms of accuracy, showing improvements ranging from 1.28% to 1.46%, approximately achieving 99.37% accuracy.

Artificial intelligence schemes to predict the mechanical performance of lignocellulosic fibers with unseen data to enhance the reliability of biocomposites

PurposeThis work introduces an integrated artificial intelligence schemes to enhance accurately predicting the mechanical properties of cellulosic fibers towards boosting their reliability for more sustainable industries.Design/methodology/approachFuzzy clustering and stacked method approach were utilized to predict the mechanical performance of the fibers. A reference dataset contains comprehensive information regarding mechanical behavior of the lignocellulosic fibers was compiled from previous experimental investigations on mechanical properties for eight different fiber materials. Data encompass three key factors: Density of 0.9–1.6 g/cm3, Diameter of 5.9–1,000 µm, and Microfibrillar angle of 2–49 deg were utilized. Initially, fuzzy clustering technique was utilized for the data. For validating proposed model, ultimate tensile strength and elongation at break were predicted and then examined against unseen new data that had not been used during model development.FindingsThe output results demonstrated remarkably accurate and highly acceptable predictions results. The error analysis for the proposed method was discussed by using statistical criteria. The stacked model proved to be effective in significantly reducing level of uncertainty in predicting the mechanical properties, thereby enhancing model’s reliability and precision. The study demonstrates the robustness and efficacy of the stacked method in accurately estimating mechanical properties of lignocellulosic fibers, making it a valuable tool for material scientists and engineers in various applications.Originality/valueCellulosic fibers are essential for biomaterials to enhance developing green sustainable bio-products. However, such fibers have diverse characteristics according to their types, chemical composition and structure causing inconsistent mechanical performance. This work introduces an integrated artificial intelligence schemes to enhance accurately predicting the mechanical properties of cellulosic fibers towards boosting their reliability for more sustainable industries. Fuzzy clustering and stacked method approach were utilized to predict the mechanical performance of the fibers.

Validation on selected breast cancer drugs of physicochemical features by using machine learning models

Breast cancer is one of the leading causes of death among females today. The elbow approach determines the ideal number of clusters after determining that the Dataset is highly cluster able with the Hopkins statistic. Three distinct groups with distinct differences were produced using the dataset's proposed expectation maximization fuzzy k-means clustering algorithm (PEMFKM). Different fuzzy clustering techniques, such as fuzzy k-means (FKM), fuzzy k-means with entropy (FKM.ENT), fuzzy k-means with entropy and noise (FKM.ENT.NOISE), Gustafson and Kessel - like fuzzy k-means (FKM.GK), Gustafson and Kessel - like fuzzy k-means with entropy regularization (FKM.GK.ENT), Gustafson and Kessel - like fuzzy kmeans with entropy regularization and noise (FKM.GK.ENT.NOISE), and PEMFKM, are evaluated. The partition coefficient (PC), partition entropy (PE), and Modified partition coefficient index (MPC) index values are better for FKM.GK than the suggested PEMFKM method. When compared to the FKM.GK method, the index values for the proposed PEMFKM algorithm have superior results for the parameters Silhouette (SIL), Xie and Beni index (XB), and fuzzy silhouette index (SIL.F). The results shows that the PEMFKM algorithm will provide better clusters and that the drugs in a given cluster may be combined for use in combination therapy for breast cancer treatment.

Building the interpolating model for interval time series based on the fuzzy clustering technique

Building the interpolating model for interval time series based on the fuzzy clustering technique

Improving power cable partial discharge pattern recognition through gustafson-kessel fuzzy clustering techniques

Existing methods for recognizing partial discharge patterns in power cables do not utilize fuzzy clustering of the discharge signals, resulting in poor quality and low recall and precision of the pattern recognition. To address this, we propose a new approach for partial discharge pattern recognition in cables using Gustafson-Kessel(GK) Fuzzy Clustering. The method involves acquiring signals from a power cable partial discharge monitoring system and then processing the signals with GK fuzzy clustering. The clustered discharge signals are filtered with wavelet packet transforms before input into an improved adaptive resonance theory(ART) neural network for final pattern recognition. Experiments demonstrate the new technique achieves up to 98.7% recall and 85.6% precision for discharge pattern recognition, with discharge signal Signal Noise Ratio(SNR) between 55 dB and 62 dB and maximum recognition accuracy reaching 98%. The proposed fuzzy clustering-based pattern recognition approach significantly enhances partial discharge diagnostics for power cable monitoring.

Effect of Friction Coefficient in Friction Stir Welding of B4C Reinforced AA5083 Metal Matrix Composites and Use of Fuzzy Clustering Technique for Weld Strength Prediction

The friction stir welding (FSW) method was used to weld B4C reinforced AA 5083 metal matrix composites in this study. By coating titanium nitride (TiN), aluminium chromium nitride (AlCrN), and diamond-like carbon (DLC) to a thickness of 4 microns, three FSW tools with square pin profiles were developed and the friction coefficients of 0.69, 0.32, and 0.2 were maintained. At three levels, the process factors such as tool rotating speed, transverse feed, and axial force were examined. For each tool, 15 samples were made using the central composite design. The influence of the friction coefficient on ultimate tensile strength, microstructural features, and tool condition was studied, and the flower pollination algorithm (FPA) technique was used to find the best process parameters for obtaining maximum ultimate tensile strength of FSW joints. The improved tensile strength of FSW joints was verified using a validation test. The coating has a considerable influence on the ultimate tensile strength, microstructure, and tool condition, according to the results of the tool’s friction coefficient. The results on the prediction of strength using the fuzzy clustering technique showed that the technique is effective in predicting the tensile strength values, with the root mean square error (RSME) of TiN, AlCrN, and DLC being 0.0027, 0.0016, and 0.0015, respectively, and the low RSME indicating that the prediction based on the fuzzy subtractive clustering technique is perfect and effective.

Fuzzy Soft Set Clustering for Categorical Data

Categorical data clustering is difficult because categorical data lacks natural order and can comprise groups of data only related to specific dimensions. Conventional clustering, such as k-means, cannot be openly used to categorical data. Numerous categorical data using clustering algorithms, for instance, fuzzy k-modes and their enhancements, have been developed to overcome this issue. However, these approaches continue to create clusters with low Purity and weak intra-similarity. Furthermore, transforming category attributes to binary values might be computationally costly. This research provides categorical data with fuzzy clustering technique due to soft set theory and multinomial distribution. The experiment showed that the approach proposed signifies better performance in purity, rank index, and response times by up to 97.53%. There are many algorithms that can be used to solve the challenge of grouping fuzzy-based categorical data. However, these techniques do not always result in improved cluster purity or faster reaction times. As a solution, it is suggested to use hard categorical data clustering through multinomial distribution. This involves producing a multi-soft set by using a rotated based soft set, and then clustering the data using a multivariate multinomial distribution. The comparison of this innovative technique with the established baseline algorithms demonstrates that the suggested approach excels in terms of purity, rank index, and response times, achieving improvements of up to ninety-seven-point fifty three percent compared to existing methods.

Fuzzy C-mean clustering technique based visual features fusion for automatic video summarization method

Fuzzy C-mean clustering technique based visual features fusion for automatic video summarization method

Detecting IoT Anomalies Using Fuzzy Subspace Clustering Algorithms

There are many applications of anomaly detection in the Internet of Things domain. IoT technology consists of a large number of interconnecting digital devices not only generating huge data continuously but also making real-time computations. Since IoT devices are highly exposed due to the Internet, they frequently meet with the challenges of illegitimate access in the form of intrusions, anomalies, fraud, etc. Identifying these illegitimate accesses can be an exciting research problem. In numerous applications, either fuzzy clustering or rough set theory or both have been successfully employed. As the data generated in IoT domains are high-dimensional, the clustering methods used for lower-dimensional data cannot be efficiently applied. Also, very few methods were proposed for such applications until today with limited efficacies. So, there is a need to address the problem. In this article, mixed approaches consisting of nano topology and fuzzy clustering techniques have been proposed for anomaly detection in the IoT domain. The methods first use nano topology of rough set theory to generate CORE as a subspace and then employ a couple of well-known fuzzy clustering techniques on it for the detection of anomalies. As the anomalies are detected in the lower dimensional space, and fuzzy clustering algorithms are involved in the methods, the performances of the proposed approaches improve comparatively. The effectiveness of the methods is evaluated using time-complexity analysis and experimental studies with a synthetic dataset and a real-life dataset. Experimentally, it has been found that the proposed approaches outperform the traditional fuzzy clustering algorithms in terms of detection rates, accuracy rates, false alarm rates and computation times. Furthermore, nano topological and common Mahalanobis distance-based fuzzy c-means algorithm (NT-CM-FCM) is the best among all traditional or nano topology-based algorithms, as it has accuracy rates of 84.02% and 83.21%, detection rates of 80.54% and 75.37%, and false alarm rates of 7.89% and 9.09% with the KDDCup’99 dataset and Kitsune Network Attack Dataset, respectively.

Construction and application of a performance assessment model for teaching operations research based on Bayesian network technology

Abstract Operations research plays a pivotal role in fostering effective management, sound decision-making, and modern management practices. Its significance in the academic realm, particularly within higher education, is increasingly acknowledged. This study leverages a Bayesian network to introduce a Markov chain, thereby constructing a dynamic Bayesian network-based model for evaluating the teaching performance of operations research. Initially, a questionnaire survey method was utilized to make preliminary adjustments to the performance evaluation index system. Subsequently, various methods, including the principle of halves and the coefficient of variation, were applied for coarse and fine screening of indices, culminating in the development of a comprehensive index system for evaluating operations research teaching performance. The efficacy of the constructed model and index system was tested by assessing the performance of five operations research instructors. Key metrics such as teaching performance, student trust, and student interest were assigned the highest weights, with respective values of 0.153, 0.127, and 0.114. Notably, Teacher D achieved the highest total performance score of 9.051, surpassing the other four instructors. The assessment results obtained from the designed model are closely aligned with those derived from fuzzy clustering techniques, underscoring the model’s robustness and applicability. Thus, the performance assessment model and index system designed for operations research teaching work demonstrate substantial practical effectiveness.

Innovation and Practice of Enterprise Short Video Operation and Management Mode in Melting Media Environment

Abstract In the contemporary integrated media environment, the innovation of enterprise short video operation and management, as well as the strategic utilization of modern network technologies, are pivotal in amplifying an enterprise’s influence among users on burgeoning integrated media platforms. This study delves into refining the existing recommendation algorithms, which are crucial for enhancing user engagement and retention rates in enterprise short video services. By integrating the Vulture Search Algorithm to optimize the Fuzzy C-Means user clustering technique and leveraging user profiles developed from questionnaire surveys, this research pioneers a novel approach for tailoring the short video recommendation processes. The enhanced algorithm effectively addresses research needs aligned with user pReferencesand enhances the Fuzzy C-Means method. The application of this refined recommendation algorithm demonstrated significant improvements in user stickiness, driven by the recommendations’ accuracy, engagement, enjoyment, and novelty. Factor analysis revealed that these attributes account for 89.553% of the total variance, which highlights their significant impact on user engagement. Consequently, enterprises seeking to innovate their short video operations within the integrated media landscape must prioritize optimizing video recommendation systems to foster user attachment and satisfaction.

Sustainable self-consolidating green concrete: neural-network and fuzzy clustering techniques for cement replacement

Sustainable self-consolidating green concrete: neural-network and fuzzy clustering techniques for cement replacement

Handling incomplete data using Radial basis Kernelized Intuitionistic Fuzzy C-Means

Missing data imputation is a critical task in the data pre-processing stage to ensure the quality, stability, and reliability of machine learning models. If missing values are imputed incorrectly, it can result in erroneous predictions and inconsistent model performance. Traditional imputation methods often struggle with complex data patterns having non-linearity and uncertainties. By integrating soft clustering principles, the proposed work provides a flexible framework for imputing missing data by taking into account the underlying inherent data pattern. To address the missing data challenges, this paper presents a novel imputation technique called Radial Basis Kernel Intuitionistic Fuzzy Ⅽ-Means Imputation (KIFCMI), which builds upon the standard Intuitionistic Fuzzy Ⅽ-Means (IFCM) technique. KIFCMI explores the application of centroid-based imputation using IFCM by incorporating the RBF kernel-induced metric in the data space as a replacement for the original Euclidean norm metric. By employing a kernel function, KIFCMI enables the clustering of data that lacks linear separability within the original space, enabling the formation of homogeneous clusters in a space of higher dimensionality. The effectiveness of the proposed imputation technique is validated on ten diverse real-world datasets obtained from the Public Library UCI with 10% and 20% missing data. The comparative analyses of the proposed technique KIFCMI, is carried out with three conventional techniques, namely fuzzy c-means imputation (FCMI), kernelized fuzzy c-means imputation (KFCMI), and intuitionistic fuzzy c-means imputation (IFCMI). The experimental results using two performance measures, namely RMSE and MAE, showcase the robustness and versatility of the proposed technique across other imputation outcomes. This research paper contributes to the evolving landscape of missing data imputation, offering insights into the practical applications of fuzzy clustering techniques.

Chemical composition of arsenic-based acid mine drainage in the downstream of a gold mine: Fuzzy regression and clustering analysis

This study employs fuzzy regression and fuzzy multivariate clustering techniques to analyze arsenic-polluted water samples originating from acid rock drainage in waste rock dumps. The research focuses on understanding the complex relationships between variables associated with arsenic contamination, such as water arsenic concentration, pH levels, and soil characteristics. To this end, fuzzy regression models were developed to estimate the relationships between water arsenic concentration and independent variables, thus, incorporating the inherent uncertainties into the analysis. Furthermore, multivariate fuzzy k-means clustering analysis facilitated the identification of fuzzy-based clusters within the dataset, providing insights into spatial patterns and potential sources of arsenic pollution. The pairwise comparisons indicated the strongest correlation of 0.62 between soil total arsenic and pH, while the weakest correlation of 0.13 was observed between soil-soluble arsenic and soil iron, providing valuable insights into their relationships and impact on water arsenic levels. The associated uncertainties in the relationships among the variables were determined based on the degree of belongingness of each data point to various fuzzy sets. Three distinct clusters emerged from the analysis: Cluster 1 comprised Points 5, 6, and 7; Cluster 2 included Points 1, 2, 3, 4, 8, and 9; and Cluster 3 consisted of Points 10, 11, 12, and 13. The findings enhance our understanding of the factors influencing arsenic contamination to provide an effective mitigation strategy in acid rock drainage scenarios. This research also demonstrates the applicability and effectiveness of fuzzy regression and fuzzy multivariate clustering in the analysis of arsenic-polluted water samples.

Hybrid fuzzy clustering technique to enhance the performance based on a fusion of intuitionistic modified fuzzy c-means and improved genetic algorithm

Hybrid fuzzy clustering technique to enhance the performance based on a fusion of intuitionistic modified fuzzy c-means and improved genetic algorithm