Fuzzy Clustering Research Articles

Trust Aware Nero Fuzzy Based Agglomerative Hierarchical Clustering with Secure Whale Optimization Routing for Enhancing Energy Efficiency in WSN

Wireless sensor networks (WSNs) comprise a network of dispersed, carefully positioned sensor nodes in their deployment environment to monitor and collect data on natural phenomena. These sensor nodes collaborate to transmit data via multi-hop communication, ultimately reaching a central base station for processing. However, WSNs face significant challenges due to the resource-constrained nature of these devices and the harsh, open environments in which they operate. Addressing energy optimization and ensuring secure communication are primary concerns in the successful operation of WSNs. This paper introduces anovelTrust aware Neuro Fuzzy Clustering head selection (TNFCH) and agglomerative hierarchical clustering approach (AHC) with Secure Whale Optimization (SWO) Algorithm Routing to enhance energy-efficient transmission in WSNs. Our proposed protocol (TNFCH-AHWO) efficiently organizes nodes by utilizing neural network and Fuzzy logic then securely transfers the data into the communication network. We employ a Trust calculation algorithm in our system to ensure Trust and data integrity, facilitating efficient lightweight operations such as key generation, encryption, decryption, and verification. This ensures hop-to-hop authentication among the nodes in WSNs. To assess the performance of our proposed protocol, we conducted simulations using the NS3 simulator. The findings of the simulation show that the suggested protocol greatly enhances various performance metrics, including energy consumption analysis, throughput, network delay, network lifetime, and packet delivery ratio when compared to existing protocols.

Exoplanet host star classification: multi-objective optimization of incomplete stellar abundance data

Abstract The presence of a planetary companion around its host star has been repeatedly linked with stellar properties, affecting the likelihood of substellar object formation and stability in the protoplanetary disc, thus presenting a key challenge in exoplanet science. Furthermore, abundance and stellar parameter data sets tend to be incomplete, which limits the ability to infer distributional characteristics harnessing the entire data set. This work aims to develop a methodology using machine learning (ML) and multi-objective optimization for reliable imputation for subsequent comparison tests and host star recommendation. It integrates fuzzy clustering for imputation and ML classification of hosts and comparison stars into an evolutionary multi-objective optimization algorithm. We test several candidates for the classification model, starting with a binary classification for giant planet hosts. Upon confirmation that the eXtreme Gradient Boosting algorithm provides the best performance, we interpret the performance of both the imputation and classification modules for binary classification. The model is extended to handle multilabel classification for low-mass planets and planet multiplicity. Constraints on the model’s use and feature/sample selection are given, outlining strengths and limitations. We conclude that the careful use of this technique for host star recommendation will be an asset to future missions and the compilation of necessary target lists.

Type2 soft biclustering framework for Alzheimer microarray

Microarray technology is a powerful tool that enables simultaneous analysis of the expression level of a large number of genes for different samples. Reliable information on gene expression level is much needed in the health system as it is widely used to predict, diagnose, and treat human diseases (e.g., Alzheimer's). For the analysis of the microarray dataset, biclustering is known to be a highly capable approach, however some characteristics of the dataset including high dimensionality, noise, uncertainty, and complex biological processes need to be handled properly. Concerning these characteristics, the current paper proposes a novel two-stage biclustering framework based on soft clustering and a metaheuristic technique. The integration of the two stages ensures a reliable search process to find similar expression patterns concerning gene expression characteristics. The proposed framework employs fuzzy and possibilistic clustering along with Type2-Fuzzy Sets theory to handle high-level uncertainty, noise, and outliers in microarray datasets. Considering the NP-hard nature of the biclustering method, the proposed framework incorporates the Genetic Algorithm with a unique chromosome representation, fitness function, and modification mechanisms. Real microarray datasets of Alzheimer's Disease have been used to evaluate the proposed framework. The comparative analysis of different versions of our proposed framework and some well-known biclustering methods demonstrates that the proposed framework is superior in terms of some indices including the mean squared residual and variance indices. The final results are further evaluated using the defined fitness function, which indicates the better performance of our possibilistic-based biclustering methods.

Adaptive fuzzy weighted C-mean image segmentation algorithm combining a new distance metric and prior entropy

The fuzzy C-mean clustering algorithm (FCM) is effective in image segmentation. However, its sensitivity to initialization settings and the limitation of the Euclidean distance metric in measuring similarity lead to unsatisfactory image segmentation. To overcome these shortcomings, we propose an adaptive fuzzy weighted C-mean image segmentation algorithm that combines a new distance metric and prior entropy. This algorithm significantly contributes in three aspects. First, we employ an improved sparse subspace clustering (SSC) algorithm based on the superpixel image for initialization settings. This step aims to obtain the initial number of clusters and the membership matrix, ensuring the algorithm’s convergence to the optimal solution. Second, to better capture differences between image regions, we introduce a novel distance metric that combines Euclidean and non-Euclidean distances. Simultaneously, the fuzzy weight is introduced to mitigate redundant feature interference, making the clustering result more reasonable. Finally, leveraging prior entropy – a maximum entropy under conditional constraints – we refine the algorithm’s adaptability to unknown features, thereby improving clustering accuracy. Comparative experiments with several state-of-the-art algorithms validate the effectiveness and robustness of our proposed algorithm.

Multi-view fuzzy C-means clustering with kernel metric and local information for color image segmentation

PurposeMulti-view fuzzy clustering algorithms are not widely used in image segmentation, and many of these algorithms are lacking in robustness. The purpose of this paper is to investigate a new algorithm that can segment the image better and retain as much detailed information about the image as possible when segmenting noisy images.Design/methodology/approachThe authors present a novel multi-view fuzzy c-means (FCM) clustering algorithm that includes an automatic view-weight learning mechanism. Firstly, this algorithm introduces a view-weight factor that can automatically adjust the weight of different views, thereby allowing each view to obtain the best possible weight. Secondly, the algorithm incorporates a weighted fuzzy factor, which serves to obtain local spatial information and local grayscale information to preserve image details as much as possible. Finally, in order to weaken the effects of noise and outliers in image segmentation, this algorithm employs the kernel distance measure instead of the Euclidean distance.FindingsThe authors added different kinds of noise to images and conducted a large number of experimental tests. The results show that the proposed algorithm performs better and is more accurate than previous multi-view fuzzy clustering algorithms in solving the problem of noisy image segmentation.Originality/valueMost of the existing multi-view clustering algorithms are for multi-view datasets, and the multi-view fuzzy clustering algorithms are unable to eliminate noise points and outliers when dealing with noisy images. The algorithm proposed in this paper has stronger noise immunity and can better preserve the details of the original image.

A New Criterion for Improving Convergence of Fuzzy C-Means Clustering

One of the most used algorithms to solve the fuzzy clustering problem is Fuzzy C-Means; however, one of its main limitations is its high computational complexity. It is known that the efficiency of an algorithm depends, among other factors, on the strategies for its initialization and convergence. In this research, a new convergence strategy is proposed, which is based on the difference of the objective function values, in two consecutive iterations, expressed as a percentage of its value in the next to the last one. Additionally, a new method is proposed to optimize the selection of values of the convergence or stop threshold of the algorithm, which is based on the Pareto principle. To validate our approach, a collection of real datasets was solved, and a significant reduction in the number of iterations was observed, without affecting significantly the solution quality. Based on the proposed method and the experiments carried out, we found it is convenient to use threshold values equal to 0.73 and 0.35 if a decrease in the number of iterations of approximately 75.2% and 64.56%, respectively, is wanted, at the expense of a reduction in solution quality of 2% and 1%, respectively. It is worth mentioning that, as the size of the datasets is increased, the proposed approach tends to obtain better results, and therefore, its use is suggested for datasets found in Big Data and Data Science.

The Impact of Big Data Analytics Capabilities on the Diversification of E-Commerce Firms

Abstract With the arrival of the big data era, e-commerce enterprises are facing more and more competition and market changes. A fuzzy clustering model is created by combining fuzzy set theory and fuzzy logic with a clustering algorithm in this paper. Then, for the shortcomings of the fuzzy C-mean clustering algorithm, the genetic simulated annealing algorithm is used to weight the data points, the fuzzy C-mean clustering model is improved, and a fuzzy clustering algorithm based on the genetic simulated annealing optimization is proposed. By using this algorithm, e-commerce enterprises’ diversification is being analyzed. The results show that the mean and standard deviation of each supply chain inventory, production orders, supplier ordering, and platform ordering quantity after optimization are reduced to different degrees, some of which reach more than 60%. Stabilizing the development of e-commerce enterprises can be achieved by reducing supply chain inventory and order quantity. E-commerce enterprises that excel in their enterprise production quality (0.741), credibility (0.748), and product sales competitiveness (0.726) are more competitive. E-commerce enterprises can utilize the ability of big data analysis to achieve diversification.

Design of a neuro-fuzzy model for agricultural employment in Colombia using fuzzy clustering

<p>High levels of poverty in rural areas constitute one of the main challenges for developing countries. Since agricultural employment is the main source of income in these areas, the design of tools that simulate and help public policymakers will be remarkably useful. This work proposes the development of a model for agricultural employment in Colombia, considering input variables such as education, contract, and income, and the output is the amount of agricultural employment. Real data measured in Colombia are used for the design and adjustment of the model. To design the fuzzy system for an agricultural employment model, the methods employed are fuzzy C-means clustering and neuro-fuzzy systems. The systems were tested with different cluster configurations, and a fuzzy system was obtained with an adequate distribution of the fuzzy sets and the respective rules that relate the sets. It was observed that as the clusters increase, the adjustment function decreases. The implementation of neuro-fuzzy systems to model agricultural employment will allow public policymakers to generate guidelines that adjust to their political agendas with a lower degree of uncertainty.</p>

2-SAT discrete Hopfield neural networks optimization via Crow search and fuzzy dynamical clustering approach

<abstract> <p>Within the swiftly evolving domain of neural networks, the discrete Hopfield-SAT model, endowed with logical rules and the ability to achieve global minima of SAT problems, has emerged as a novel prototype for SAT solvers, capturing significant scientific interest. However, this model shows substantial sensitivity to network size and logical complexity. As the number of neurons and logical complexity increase, the solution space rapidly contracts, leading to a marked decline in the model's problem-solving performance. This paper introduces a novel discrete Hopfield-SAT model, enhanced by Crow search-guided fuzzy clustering hybrid optimization, effectively addressing this challenge and significantly boosting solving speed. The proposed model unveils a significant insight: its uniquely designed cost function for initial assignments introduces a quantification mechanism that measures the degree of inconsistency within its logical rules. Utilizing this for clustering, the model utilizes a Crow search-guided fuzzy clustering hybrid optimization to filter potential solutions from initial assignments, substantially narrowing the search space and enhancing retrieval efficiency. Experiments were conducted with both simulated and real datasets for 2SAT problems. The results indicate that the proposed model significantly surpasses traditional discrete Hopfield-SAT models and those enhanced by genetic-guided fuzzy clustering optimization across key performance metrics: Global minima ratio, Hamming distance, CPU time, retrieval rate of stable state, and retrieval rate of global minima, particularly showing statistically significant improvements in solving speed. These advantages play a pivotal role in advancing the discrete Hopfield-SAT model towards becoming an exemplary SAT solver. Additionally, the model features exceptional parallel computing capabilities and possesses the potential to integrate with other logical rules. In the future, this optimized model holds promise as an effective tool for solving more complex SAT problems.</p> </abstract>

Fuzzy time series clustering using autoencoders neural network

<p>This study presents a novel approach that employs autoencoders (AE)—an artificial neural network—for the nonlinear transformation of time series to a compact latent space for efficient fuzzy clustering. The method was tested on atmospheric sea level pressure (SLP) data towards fuzzy clustering of atmospheric circulation types (CTs). CTs are a group of dates with a similar recurrent SLP spatial pattern. The analysis aimed to explore the effectiveness of AE in producing and improving the characterization of known CTs (i.e., recurrent SLP patterns) derived from traditional linear models like principal component analysis (PCA). After applying both PCA and AE for the linear and nonlinear transformation of the SLP time series, respectively, followed by a fuzzy clustering of the daily SLP time series from each technique, the resulting CTs generated by each method were compared to assess consistency. The findings reveal consistency between the SLP spatial patterns from the two methods, with 58% of the patterns showing congruence matches greater than 0.94. However, when examining the correctly classified dates (i.e., the true positives) using a threshold of 0.8 for the congruence coefficient between the spatial composite map representing the CT and the dates grouped under the CT, AE outperformed PCA with an average improvement of 29.2%. Hence, given AE's flexibility and capacity to model complex nonlinear relationships, this study suggests that AE could be a potent tool for enhancing fuzzy time series clustering, given its capability to facilitate the correct identification of dates when a given CT occurred and assigning the dates to the associated CT.</p>

Understanding the intra-day and intra-week ridership patterns of urban rail transit stations in London using a fuzzy clustering approach

The needs for transit station classification are ever-growing as the planning process, be it at a strategic or operational level, becomes increasingly automated, data-oriented, and short-cycled. Whilst most existing models have used binary methods, this study applied a fuzzy clustering approach and examined cluster memberships (i.e., to what degree a station belongs to each cluster) of London rail transit stations by using entry and exit data with intra-day and intra-week variations. A method of hyperparameter selection in fuzzy clustering considering the context of transportation and a framework of ridership variation analysis was proposed. The results suggest that fuzzy clustering can maximise the information from high-resolution temporal passenger flow data of urban rail transit. The membership breakdowns allow users to have a better understanding of station characteristics and help to avoid inadequate plans by treating the stations belonging to multiple clusters in a different manner from the binary clustering, where each station only belongs to one cluster. Furthermore, fuzzy clustering can capture the ridership variation patterns and reveal special clusters. The results can be potentially applied in operation planning, such as service timetabling, station staff working-hour designs and fare strategy designs, etc.

Research on Quality Evaluation of Smart Education Schools Based on Fuzzy Clustering Algorithm and Strategies for Future Development

Abstract The burgeoning field of wisdom education has led to a disparity in the quality of educational institutions, thereby necessitating enhancements to the existing school quality evaluation systems. This study refines the traditional fuzzy C-mean clustering algorithm by incorporating a Gaussian mixture distribution and information entropy regularization. It then employs this enhanced algorithm to construct a quality evaluation system for smart education schools. The improved fuzzy C-mean clustering algorithm demonstrated in this paper significantly outperforms the standard C-means used at SCU and the K-means algorithm in terms of Mean Absolute Error (MAE) and Total Hit Rate (THR). Specifically, the optimized algorithm decreased the MAE to 0.572 from 0.619, a reduction of 16.01% compared to the K-means algorithm. It increased the THR to 0.158 from 0.147, marking an improvement of 17.04% over the Kmeans algorithm. Furthermore, the refined clustering algorithm achieved an impressive accuracy of 95.71%. Analysis through this clustering approach reveals that the developed quality evaluation system accurately reflects the operational level of smart education within schools. The establishment of such a system is anticipated to significantly guide the future progression of smart education and aid in the foundational development of smart education schools.

A Comparative Study of K-Medoids and Fuzzy K-Means Clustering for the Selection of Optimal Cloud Service Provider

The wide range of services offered by cloud service providers, including data processing, storage and computing resources, makes it difficult to choose the best provider for a particular application. A workable approach is provided by clustering algorithms, which group cloud service providers according to common characteristics including price, reliability, security, and performance. Among all clustering techniques, fuzzy K-means and K-medoids stand out as popular choices. Non-parametric K-means finds cluster representatives called Medoids, while fuzzy K-means soft clustering method places data points into several clusters with different membership levels. Using these algorithms makes it easy to compare and select the cloud service provider best suited for a particular application. The current research examines the application of K-medoids and fuzzy K-means clustering techniques and its performance matrix such as Silhouette Score, Cluster Cohesion, Cluster Separation, Calinski-Harabasz Index(CH), Davies-Bouldin Index(DBI), Inertia and Adjusted Rand Index (ARI) in cloud service provider selection.

The Path of Digital Protection and Innovative Development of Rural Traditional Cultural Resources Supported by Intelligent Information

Abstract The digitized quality of rural traditional cultural resources is relatively low, and there are problems such as heterogeneity and incompleteness in the resource data, resulting in the limitation of related cultural data information mining and the inability to realize deep development and innovation. Therefore, this paper combines the fully connected neural network and fuzzy C-mean clustering algorithm to construct a cultural digital resource clustering model, and based on the clustering results combined with CR-LDA and collaborative filtering algorithm to achieve personalized recommendation of rural traditional cultural digital resources. The experimental results show that the clustering model combining a fully connected neural network and a fuzzy C-mean clustering algorithm has a better clustering effect than the other three clustering models, and it also shows good robustness and stability. In addition, although the collaborative filtering model combining the CR-LDA algorithm has a slight increase in runtime compared to the LDA model runtime, the classification accuracy is significantly improved. It thus can provide platform users with practical and reliable cultural resource recommendations. Most users indicated in the survey that they agreed with the results of the model’s personalized cultural digital resource recommendations.

Prediction of Historical Development Trends of Traditional Wushu Culture Based on Data Mining

Abstract This paper first introduces the use of data mining technology in the development of the traditional culture of martial arts. This paper begins with the optimized FCM algorithm, obtains the fuzzy pattern through the affiliation function, and builds a prediction model using the clustering algorithm of fuzzy time series. The historical development trend of Chinese martial arts traditional culture is predicted using this model. The results show that the process of defuzzification prediction divides the literature on the development of traditional culture of martial arts into five groups, and the five clustering centers are A1: 3055-4693, A2: 5603-6919, A3: 6388-7497, A4: 7984-8150, and A5: 8876-9483. The predicted values of the FCM algorithm model for the literature of the first group to the fifth group are respectively 3735.3, 5374.05, 6351.57, 7048.56, and 9144.31. The average error of prediction is 0.134, 0.062, 0.094, 0.126, and 0.025, respectively. The average prediction accuracies of the predictions are all >85%, and in particular, the accuracy of predicted values for the fifth group reaches 98%. It can be seen that the prediction model proposed in this paper is effective in predicting the historical development trend of the traditional culture of martial arts.

Privacy Risk Assessment of Medical Big Data Based on Information Entropy and FCM Algorithm

Privacy Risk Assessment of Medical Big Data Based on Information Entropy and FCM Algorithm

User Behavior Analysis and Optimization of Japanese Language Online Education Platforms

Abstract Japanese language instruction at universities has gained new life thanks to the quick growth of online learning via the Internet, microclasses, flipped classrooms, and other innovative teaching methods. This is the direction of future educational reform in colleges and universities. After gathering and pre-processing behavioral data of Japanese learners, this study builds a data analysis model of Japanese online learning user behavior based on the Japanese online education platform. In this model, user behavioral features are extracted and classified using the RFG-SVM model, which is based on SVM. Users with similar user behaviors are then clustered together using the FCM algorithm, and the association rule algorithm is utilized to explore the intricate relationship between user online learning behaviors and learning effects. Lastly, the FSQCA approach is used to investigate the optimization path of Japanese online education platforms after combining with example analysis. The most significant aspect of Japanese online learning is its online learning rate (0.7499). Users can be categorized into three groups: close cooperation (52.3%), active participation (6.1%), and weak participation (41.6%). Japanese online learners also exhibit better user behavior. The consistency indexes for Grouping H1: SI*LA*CC*HA, Grouping H2: ~SQ*CQ*SI*~LA*CC, and Grouping H3: SQ*CQ*SI*~LA*HA were 0.929, 0.959, and 0.965, respectively, with both social influence (SI) and habit (HA) serving as important requisites. This study helps to form a mature mechanism that prompts Japanese online education users to develop a continuous willingness to use the program, which contributes to the development of inclusive education to a certain extent.

Image Change Detection Based on Fuzzy Clustering and Neural Networks

Image Change Detection Based on Fuzzy Clustering and Neural Networks

Customer Relationship Value Evaluation Method for E-commerce Platform Based on Fuzzy Clustering

Customer Relationship Value Evaluation Method for E-commerce Platform Based on Fuzzy Clustering

INTUITIONISTIC ROBUST CLUSTERING FOR SEGMENTATION OF LESIONS IN DERMATOSCOPIC IMAGES

This paper presents the formulation of the intuitive fuzzy clustering algorithm to be robust to atypical data present in dermoscopic images and to delimit the affected area. This algorithm is formulated from the objective function derivation for memberships update, to integrate an m-redescending estimator influence function. Experimentation shows an accuracy of 95% with the proposal algorithm with respect to other clustering algorithms to perform delimitations, in addition the iterations number is considerably reduced. Keywords: Robust Intuitionistic Fuzzy Clustering, Dermoscopic Images, Delimitations of Lesions, M-redescending Estimator