Fuzzy Clustering Research Articles

Spatial Variation of Population Density and Age Structure of White-Tailed Deer (Odocoileus virginianus) in a Colombian Moor

Wildlife population density is affected by resource availability, predators and competitors, and anthropogenic stressors. Its analysis and the factors affecting it are important for conservation, use, or population control. Based on estimates of density and population parameters, together with niche modeling, previous studies considered that the white-tailed deer population at the Chingaza National Natural Park (NNP) is undergoing exponential growth due to the marginal influence of factors regulating its abundance, such as predators. The aim of this study was to evaluate the population density and age structure of the white-tailed deer in the Chingaza NNP, Colombia. To evaluate the population density and age structure of white-tailed deer in the Monterredondo and La Paila sectors of the Chingaza NNP, 10 transects were installed with circular plots in each. These were visited twice; in each visit, we collected all deer pellet groups, measuring each pellet to estimate its volume. Population density was calculated using the Eberhardt and Van Etten method and the semi-automated PELLET procedure using FSC and FAR techniques; the age structure was estimated by k-means and fuzzy clustering analyses. With the FAR technique, the estimated deer density was 2.09 ind/km2 in Monterredondo and 0.94 ind/km2 in La Paila. With PELLET, these values were 0.37 ± 0.4 ind/km2 for Monterredondo and 0.16 ± 0.2 ind/km2 for La Paila. The proportion of age classes (adult, juvenile, and fawn) was 1:0.29:0 for Monterredondo and 1:0.57:0.04 for La Paila. The population density of white-tailed deer in two sectors of the Chingaza NNP is not homogeneous, but shows temporal and spatial variations. Population density has decreased versus reports from previous studies in this same area, with the highest deer abundance recorded in 2004, mostly in the Monterredondo sector. The proportion of age classes suggests that the population is stable, as the largest proportion of individuals corresponds to the adult stage

Cluster-Centered Visualization Techniques for Fuzzy Clustering Results to Judge Single Clusters

Fuzzy clustering, as a powerful method for pattern recognition and data analysis, often produces complex results that require careful examination of individual clusters. In this paper, advanced visualization techniques are presented that aim to facilitate the analysis of fuzzy clustering results by focusing on the evaluation and interpretation of individual clusters. The presented approach is based on the development of cluster-centric visualization techniques that consider the inherent uncertainty of fuzzy clustering results. The novelty is an assessment of individual clusters with the proposed visualizations. In general, three cluster-centered visualization techniques are presented. These approaches are intended not only to illustrate the overall structure of the fuzzy clustering results but also to enable detailed individual cluster analysis. The performance of the presented visualization techniques is demonstrated by their application to real data sets from different areas. The results show that the techniques provide an effective way to judge individual clusters in fuzzy clustering results for complex data structures.

Fault identification of product design using fuzzy clustering generative adversarial network (FCGAN) model

Fault identification of product design using fuzzy clustering generative adversarial network (FCGAN) model

Construction And Performance Evaluation of Big Data Prediction Model Based on Fuzzy Clustering Algorithm in Cloud Computing Environment

In the evolving landscape of biomedical biometrics, where multimodal approaches are increasingly crucial for reliable user authentication, this research presents a comprehensive study. The primary focus is on the construction and performance evaluation of a robust big data prediction model within a cloud computing environment. The advent of big data and cloud computing has revolutionized the field of biomedical biometrics, offering immense potential for advanced data analysis and prediction. This research presents the development and evaluation of a robust prediction model for multimodal biometric data in biomedical applications. The proposed model incorporation of Reliable Discrete Variable Topology (RDVT) into the prediction model. RDVT introduces a topological data structure that enhances data reliability and ensures the integrity of multimodal biometric information. The construction and training of the prediction model are meticulously detailed, encompassing data preprocessing, feature extraction, clustering, classification, and model evaluation. Additionally, the integration of a fuzzy clustering algorithm enhances the model's ability to handle uncertainty and imprecision in biometric data. The advancement of multimodal biometrics in the biomedical field by introducing the Reliable Discrete Variable Topology (RDVT) and a big data prediction model based on a fuzzy clustering algorithm in a cloud computing environment. The model's performance is rigorously assessed through extensive experimentation, including accuracy, precision, recall, and F1-score measurements.

Retracted: Prediction Model of Mining Subsidence Parameters Based on Fuzzy Clustering

Retracted: Prediction Model of Mining Subsidence Parameters Based on Fuzzy Clustering

Online Detection and Fuzzy Clustering of Anomalies in Non-Stationary Time Series

We consider the challenge of detecting and clustering point and collective anomalies in streaming data that exhibit significant nonlinearities and seasonal structures. The challenge is motivated by detecting problems in a communications network, where we can measure the throughput of nodes, and wish to rapidly detect anomalous traffic behaviour. Our approach is to train a neural network-based nonlinear autoregressive exogenous model on initial training data, then to use the sequential collective and point anomaly framework to identify anomalies in the residuals generated by comparing one-step-ahead predictions of the fitted model with the observations, and finally, we cluster the detected anomalies with fuzzy c-means clustering using empirical cumulative distribution functions. The autoregressive model is sufficiently general and robust such that it provides the nearly (locally) stationary residuals required by the anomaly detection procedure. The combined methods are successfully implemented to create an adaptive, robust, computational framework that can be used to cluster point and collective anomalies in streaming data. We validate the method on both data from the core of the UK’s national communications network and the multivariate Skoltech anomaly benchmark and find that the proposed method succeeds in dealing with different forms of anomalies within the nonlinear signals and outperforms conventional methods for anomaly detection and clustering.

Development of a fabric classification system using drapability and tactile characteristics

AbstractWhen producing clothing using virtual fitting technology or purchasing textile and clothing products online, it is challenging to make judgments or communicate information about sensory characteristics, such as drapability and tactile sensations, as there are no clear objective indicators for these factors. Therefore, the study aims to develop a classification system for the sensory properties of fabrics using drapability and tactile characteristics as quantitative indicators. The developed system was verified through subjective evaluations by an expert group, and it was found to be meaningful in reflecting classification levels in practice. The drapability and tactile sensation (softness; TS7) of the fabric were classified using fuzzy c-means cluster analysis, and the results were confirmed through a subjective evaluation by experts. The classification system was then used to predict the classification group, constituted by drapability and tactile characteristics, from mechanical properties using an artificial neural network. The network was trained on 534 fabric samples for drapability and tactile sensation (softness), and it correctly predicted 202 samples out of 243 validation data, with a forecasting accuracy of 83.5%. The developed classification system enables predictions and judgments about subjective characteristics like fabric drapability and tactile sensation based on the mechanical property values of various samples.

Joint learning of fuzzy embedded clustering and non-negative spectral clustering

Joint learning of fuzzy embedded clustering and non-negative spectral clustering

Mathematical Modeling and Statistical Analysis of Leach Algorithm Based Cluster Head Selection Approach for Wireless Sensor Networks

In this paper, we present a comprehensive design and analysis of our proposed improved fuzzy logic-based algorithm. The algorithm utilizes a fuzzy logic system to assess node attributes such as residual energy, distance to the base station, and connectivity. By considering these parameters, the algorithm dynamically evaluates the most suitable candidates for cluster head roles, effectively distributing the energy load and promoting equitable cluster formation. An extensive performance evaluation of proposed algorithm has been conducted through simulations and comparisons with the original LEACH protocol. Our results reveal that the improved fuzzy logic-based algorithm outperforms the conventional LEACH in terms of network lifetime, energy consumption, and overall network stability. In this study, we delve into the mathematical modeling and statistical analysis of the proposed algorithm, focusing on its impact on energy efficiency, network stability, and data routing in WSNs. The Improved Fuzzy Logic-Based Cluster Head Selection Algorithm employs fuzzy logic to intelligently select cluster heads, considering parameters like residual energy, distance to the base station, and historical data. We develop mathematical models to describe the algorithm's behavior and analyze its performance through extensive simulations and statistical evaluations. The algorithm demonstrates enhanced adaptability to varying network conditions and node heterogeneity, showcasing its potential to significantly extend the operational duration of WSNs in diverse deployment scenarios. In conclusion, this paper contributes a novel perspective to the domain of WSNs by introducing an advanced fuzzy logic-based cluster head selection algorithm. The algorithm enhances the foundation of the LEACH protocol, effectively addressing energy optimization concerns and promoting prolonged network lifetime. The presented design and analysis substantiate the algorithm's superiority, offering valuable insights for researchers and practitioners working on energy-efficient routing protocols and wireless sensor network management.

Application of autoencoders artificial neural network and principal component analysis for pattern extraction and spatial regionalization of global temperature data

Spatial regionalization is instrumental in simplifying the spatial complexity of the climate system. To identify regions of significant climate variability, pattern extraction is often required prior to spatial regionalization with a clustering algorithm. In this study, the autoencoder (AE) artificial neural network was applied to extract the inherent patterns of global temperature data (from 1901 to 2021). Subsequently, Fuzzy C-means clustering was applied to the extracted patterns to classify the global temperature regions. Our analysis involved comparing AE-based and principal component analysis (PCA)-based clustering results to assess consistency. We determined the number of clusters by examining the average percentage decrease in Fuzzy Partition Coefficient (FPC) and its 95% confidence interval, seeking a balance between obtaining a high FPC and avoiding over-segmentation. This approach suggested that for a more general model, four clusters is reasonable. The Adjusted Rand Index between the AE-based and PCA-based clusters is 0.75, indicating that the AE-based and PCA-based clusters have considerable overlap. The observed difference between the AE-based clusters and PCA-based clusters is suggested to be associated with AE’s capability to learn and extract complex non-linear patterns, and this attribute, for example, enabled the clustering algorithm to accurately detect the Himalayas region as the ‘third pole’ with similar temperature characteristics as the polar regions. Finally, when the analysis period is divided into two (1901–1960 and 1961–2021), the Adjusted Rand Index between the two clusters is 0.96 which suggests that historical climate change has not significantly affected the defined temperature regions over the two periods. In essence, this study indicates both AE’s potential to enhance our understanding of climate variability and reveals the stability of the historical temperature regions.

A novel incomplete hesitant fuzzy information supplement and clustering method for large-scale group decision-making.

Clustering is an effective means to reduce the scaling of large-scale group decision-making (LSGDM). However, there are many problems with clustering methods, such as incomplete or ambiguous information usually provided by different decision makers. Traditional clustering methods may not be able to handle these situations effectively, resulting in incomplete decision-making information. Calculating the clustering centers may become very complex and time-consuming. Inappropriate distance weights may also lead to incorrect cluster assignments, and these problems will seriously affect the clustering results. This research provides a novel incomplete hesitant fuzzy information supplement and clustering approach for large-scale group decision-making in order to address the aforementioned difficulties. First, the approach takes into account the trust degradation and the inhibition of relationships of distrust in the process of trust propagation, and then it builds a global and local network of trust. A novel supplemental formula is provided that takes into account the decision-preference maker's as well as the trust-neighbor's information, allowing the decision-neighbor maker's recommendation to be realized. Therefore, an improved distance function can be proposed to calculate the weights by combining the relative standard deviation theory and selecting the selected clustering centers by using the density peaks in order to optimize the selection of clustering centers and reduce the complexity and scaling of the decision. Finally, an example is presented to demonstrate how the proposed method can be applied. The consistency index and comparison experiments are used to evaluate if the suggested approach is effective and reliable.

Relevance vector machine with hybrid kernel-based soft sensor via data augmentation for incomplete output data in sintering process

A ratio of CO and CO2 (CO/CO2) is a key indicator of sintering carbon consumption, which is difficult to be determined in real-time. Therefore, the establishment of its soft sensing model is of great practical significance. This paper proposes a novel CO/CO2 soft sensing model with incomplete output data based on relevance vector machine with hybrid kernel via data augmentation. First, a least absolute shrinkage and selection operator is employed for determining key input variables of the model, and an automatic fuzzy clustering framework is used to automatically identify multiple operating modes. Then, a relevance vector machine with hybrid kernel method is presented to model each operating mode separately. Meanwhile, considering the problem of incomplete input and output data, data augmentation is applied in modeling to enhance the model performance. Finally, the soft sensing model of CO/CO2 is formed. Experimental results and analyses using actual production data coming from the sintering production process demonstrate that the prediction performance and accuracy of the proposed model outperform some existing algorithms.

Fuzzy clustering based on distance metric under intuitionistic fuzzy environment

Fuzzy clustering based on distance metric under intuitionistic fuzzy environment

Enhanced Fuzzy Clustering for Incomplete Instance with Evidence Combination

Clustering incomplete instance is still a challenging task since missing values maybe make the cluster information ambiguous, leading to the uncertainty and imprecision in results. This article investigates an enhanced fuzzy clustering with evidence combination method based on Dempster-Shafer theory (DST) to address this problem. First, the dataset is divided into several subsets, and missing values are imputed by neighbors with different weights in each subset. It aims to model missing values locally to reduce the negative impact of the bad estimations. Second, an objective function of enhanced fuzzy clustering is designed and then optimized until the best membership and reliability matrices are found. Each subset has a membership matrix that contains all sub-instances’ membership to different clusters. The fuzzy reliability matrix is employed to characterize the reliability of each subset on different clusters. Third, an adaptive evidence combination rule based on the DST is developed to combine the discounted subresults (memberships) with different reliability to make the final decision for each instance. The proposed method can characterize uncertainty and imprecision by assigning instances to specific clusters or meta-clusters composed of several specific clusters. Once an instance is assigned to a meta-cluster, the cluster information of this instance is (locally) imprecise. The effectiveness of proposed method is demonstrated on several real-world datasets by comparing with existing techniques.

Evaluating Intra-Field Spatial Variability for Nutrient Management Zone Delineation through Geospatial Techniques and Multivariate Analysis

This research study underscores the importance of effectively managing soil nutrients in a site-specific manner to enhance crop productivity while considering the spatial variability of the soil. The objective is to identify subfields with similar soil characteristics, referred to as management zones (MZs), to promote sustainable land utilization. This study was conducted in two central pivot fields located in Southern Idaho, USA, where barley and sugar beets were grown. Soil samples were collected from each field in a grid pattern and analyzed for various chemical properties. These properties included soil pH, organic matter, cation exchange capacity, excess lime, electrical conductivity, total inorganic nitrogen, phosphorus, potassium, calcium, magnesium, zinc, iron, manganese, copper, and boron. Descriptive statistics and normality assessments were performed, and the coefficient of variation was calculated to assess the heterogeneity of soil properties, revealing significant variability. To determine the spatial variability of soil properties, ordinary kriging was used revealing diverse spatial patterns for each location and soil variable examined with moderate to strong spatial dependence. To develop the MZs, a combination of principal component analysis and fuzzy k-means clustering was utilized, and specific parameters that represented the overall variability of soil properties in each field were identified. Based on the identified parameters, two clusters were created in each field. The first management zone (MZ1) exhibited lower values of soil pH, excess lime content, and electrical conductivity compared to the MZ2. Consequently, higher crop productivity was observed in MZ1 in both fields. The biomass yields of barley and sugar beets in MZ1 surpassed those in MZ2. This study highlights the effectiveness of the methodology employed to delineate MZs, which can be instrumental in precise soil nutrient management and maximizing crop productivity.

Semi-supervised fuzzy C means based on membership integration mechanism and its application in brain infarction lesion segmentation in DWI images

In this paper, a novel semi-supervised fuzzy clustering algorithm, MFM-SFCM, based on a membership fusion mechanism is proposed for Diffusion-weighted imaging (DWI) brain infarction lesion segmentation. The proposed MFM-SFCM algorithm addresses the issue of weakened constraints and insufficient influence of labeled samples on the clustering process that arises in the semi-supervised fuzzy C-means clustering (SFCM) when emphasizing supervised information. By using a new membership fusion mechanism, MFM-SFCM eliminates this issue, greatly improving the accuracy of clustering results and accelerating convergence speed. This allows fuzzy clustering to achieve good results in the segmentation of DWI brain infarction lesions using a small amount of labeled information. The effectiveness of the MFM-SFCM algorithm is demonstrated through experiments conducted on a real-world dataset of DWI brain images.

Fuzzy clustering for feature extraction in wavelet-based fault gear identification of electrical machines

Fault detection and diagnosis in electrical machines are periodical for preventing operational interruptions and unexpected shutdowns. However, a Wavelet Feature-dependent Clustering Technique (WFCT) is introduced to address the cyclic fault detection between successive operation intervals. This technique identifies override features from the time-frequency operational wavelets throughout the machine running time. This grouping binds time and operational frequency for identifying override exceeding shutdown/ failure instances. Based on their revamping time, the identified instances are further grouped to prevent overrides in successive operational hours. The fuzzy clustering prevents variation features based on conventional to high-fuzzified extractions.

Semi-supervised possibilistic c-means clustering algorithm based on feature weights for imbalanced data

The possibilistic c-means clustering (PCM) algorithm improves the robustness of fuzzy c-means clustering (FCM) to noise and outliers by releasing the probabilistic constraint of memberships. The semi-supervised possibilistic c-means clustering (SSPCM) algorithm improves the clustering effect on datasets with imbalanced sizes by introducing a small amount of label information. However, the traditional semi-supervised algorithm still faces the problem of low utilization of supervision information for datasets with large differences in sample sizes. Moreover, the Euclidean distance, which treats features equally, cannot handle feature-imbalanced data. Therefore, this paper proposes a semi-supervised possibilistic c-means clustering algorithm based on feature weights (FW-SSPCM) by introducing the ideas of supervised centers. First, the algorithm introduces the supervised center into the objective function of the SSPCM to improve the utilization rate of supervision information and thus guide the center iteration of small clusters. Second, the feature weighting strategy is introduced in the objective function to adaptively assign feature weights according to the importance of different features in different clusters, thus improving the adaptability of the algorithm to feature-imbalanced datasets. In addition, to improve the robustness of the antinoise effect and retain additional image details, a new image segmentation algorithm based on FW-SSPCM and local information (LFW-SSPCM) is proposed by introducing local spatial information obtained by bilateral filtering. Finally, through clustering experiments on synthetic data, UCI datasets and on color images characteristic of multiple features, including imbalanced sizes, imbalanced features and strong noise injection, the clustering performances of the proposed FW-SSPCM and LFW-SSPCM proposed in this paper are significantly better than those of several related clustering algorithms.

Fuzzy clustering of the healthy life expectancy decomposition: A multi-population analysis

This work analyses healthy life expectancy, which combines information on the period life table with the age-specific disability prevalence data in a single indicator. We quantify how much mortality and disability contributed to changes in healthy life expectancy over a fixed time horizon by age group and population to understand if life expectancy is increasing faster than the decline of disability. The prominent heterogeneity in mortality and health worldwide calls for a cross-country comparative analysis. The methodology used is decomposition, which splits the difference between two times of an aggregate index by assigning the difference to its components. Besides, through a cluster analysis, we categorize countries according to the age-specific contributions of mortality and disability simultaneously, using the healthy life expectancy decomposition results. This allows us to ease the interpretation and better outline the outcomes. Since health is increasingly considered a crucial aspect of economic growth, monitoring health expectancies is essential to assess the financial sustainability of the health and social security system. Some policy implications are finally discussed.

Energy Efficient Routing Technique Using Enthalpy Ant Net Routing for Zone-Based MANETS

Routes discovery that can provide reliable data transmission in Mobile Ad-hoc Networks is challenging due to its wireless channel characteristics and dynamic transmission environment. Ad-hoc networks frequently experience link failure because nodes are mobile and their positions are not fixed. The cellular ad hoc community's dynamic nature makes it possible to analyze multi-route routing protocols specifically. One of the most trustworthy and environment-friendly routing methods is the Zone Routing Protocol used in Mobile Ad Hoc Networks. Offering timely and trustworthy communication services, however, depends critically on maintaining the Quality of Service, electricity performance, and outstanding resource management. In this research, we suggested an energy-efficient routing method for zone-based MANETs called enthalpy and net routing. First, a completely fuzzy quarter clustering based on an Energy guide is used to perform the clustering. With the help of fuzzy memberships, the AFCM set of rules permits the input of statistics for each elegance. In the second, the best route is selected using Enthalpy Ant Net Routing (EANR) while taking the following factors into account: community disconnection, channel error, buffer overflow, and contention at the link layer. The results of the experiment show that the collection of rules performs better than many existing algorithms in terms of community longevity, electricity consumption, and other metrics.