Fuzzy and ISODATA classification of face contours

Depth profiling has been performed by using Auger electron spectrometry (AES) and X-ray photoelectron spectrometry (XPS) in combination with Ar-ion sputtering. The data obtained by both surface-analytical methods have been evaluated by means of factor analysis and afterwards by applying an artificial neural network or fuzzy clustering in order to determine the compositional layering of different samples such as a Cr2O3/CrN sandwich layer, tarnish layers on a nickel based alloy and on steel, and the coating of a Si3N4 ceramic powder. The applied artificial neural network was a Kohonen network. It turned out that the method of fuzzy c-means clustering was more successful than Kohonen network due to the fact that fuzzy c-means clustering starts with more input information which can be obtained from factor analysis.

A method of information granule computation is employed to predict the contact resistance for a kind of aircraft electrical relays. The aim is to reduce the dimensional computation complexity and provide practical model for the evaluation process of reliability analysis and fault diagnosis. At first, numerical data sample is split into consecutive sequences by equal window length, and then the sequences are taken to construct information granules, following that, the information granules are combined together to form new time series with reduced dimensionality. Secondly, the fuzzy c-mean clustering method is used as a technique to extract the features of the data points within each information granule. The clustered information granule is defined on the basis of fuzzy linguistic terms to provide parameters for fuzzy inference system, and the fourth order information granular predictive model is constructed in the paper. The predictive model creates the relationship between past four states and future state on the basis of rule base that is produced from fuzzy c-mean clustering. Finally, the predictive model is exploited by introducing experiment data and defining different window length. The trend prediction results are shown and validated with some discussions are presented. The predicative model can reduce the dimension effectively, it is also practical and simple compared with other methods such as neural networks computation and regressive analysis, especially in the case of trend predication application that has highly dimensional measurement data.

The purpose of medical image segmentation is to divides the lesions image with a special meaning and background regional. The characteristics of medical images are generally more complex. There is often overlap between different regions, the edge of the area is vague. Research based on fuzzy clustering method for medical image segmentation, fuzzy C-means clustering and fuzzy kernel clustering methods were studied and discussed fully considered the spatial information, the single objective problem conversion into multi-objective problem. The experimental results show that the method use in medical image segmentation achieved good results.

Lithium batteries are increasingly used in electric vehicle applications. However, different manufacturing processes and technical constraints lead to battery inconsistency, even for batteries in the same production batch. High-rate discharging negatively affects battery consistency and results in service life reduction. A multi-parameter sorting method at high-rate operation was proposed in this study. The method was applied to sort batteries for cars. The sorted datasets were compared and analyzed by the fuzzy C-mean clustering method, the K-means clustering method, and the simulated annealing genetic algorithm. The comparisons proved that the genetic annealing algorithm was more suitable for battery classification. The clustered batteries were assembled into modules in series and parallel for experimental validation. The test results showed that the battery module cycle life was improved.

This paper presents a further investigation into computational properties of a novel fuzzy additive spectral clustering method, Fuzzy Additive Spectral clustering (FADDIS), recently introduced by authors. Specifically, we extend our analysis to ‘difficult’ data structures from the recent literature and develop two synthetic data generators simulating affinity data of Gaussian clusters and genuine additive similarity data, with a controlled level of noise. The FADDIS is experimentally verified on these data in comparison with two state‐of‐the‐art fuzzy clustering methods. The claimed ability of FADDIS to help in determining the right number of clusters is experimentally tested, and the role of the pseudo‐inverse Laplacian data transformation in this is highlighted. A potentially useful extension of the method to biclustering is introduced.

Context. The problem of clustering (classification without a teacher) is often occures when processing data arrays of various natures, which is quite an interesting and integral part of artificial intelligence. To solve this problem, there are many known methods and algorithms based on the principles of the distribution density of observations in the analyzed data. However, these methods are rather complicated in software implementation and are not without drawbacks, namely: the problem of determining significant clusters in datasets of different densities, multiepoch self-learning, getting stuck in local extrema of goal functions, etc. It should be noted that the methods based on the analysis of the peaks of the data distribution density are clear in nature, therefore, to expand the capabilities of these methods, it is advisable to introduce their fuzzy modification. Objective. The aim of the work is to introduce fast fuzzy data clustering using density peaks distribution of the datasets, that can find the prototypes (centroids) of clusters that overlapping regardless of the amount of incoming data. Method. The problem of fuzzy clustering data arrays based on a hybrid method that based on the simultaneous use of a credibilistic approach to fuzzy clustering and an algorithm for finding the types of distribution density of the initial data is proposed. A feature of the proposed method is computational simplicity and high speed, due to the fact that the entire array is processed only once, that is, eliminates the need for multi-era self-learning, implemented in traditional fuzzy clustering algorithms. Results. A feature of the proposed method of fast fuzzy credibilistic clustering using of density peaks distribution is characterized by computational simplicity and high speed due to the fact that the entire array is processed only once, that is, the need for multiepoch self-learning is eliminated, which is implemented in traditional fuzzy clustering algorithms. The results of the computational experiment confirm the effectiveness of the proposed approach in clustering problems under conditions in the case when the clusters are ovelap. Conclusions. The experimental results allow us to recommend the developed method for solving the problems of automatic clustering and data classification, as quickly as possible to find the centroids of clusters. The proposed method of fast fuzzy credibilistic clustering using of density peaks distribution of dataset is intended for use in computational intelligence systems, neuro-fuzzy systems, in training artificial neural networks and in clustering problems.

The paper presents a method for surface reconstruction from large unorganized and noisy point sets without any normal or orientation information. Firstly, the outliers will be selected and deleted, and acquire new point sets being less noisy with improved method of fuzzy c-means clustering. Secondly, we compute the normal of the new point sets through PCA analysis. Then we interpolate single-level and multi-level the new points acquired by clustering method by compactly supported radial basis function (CSRBF). The experiments show that the outliers will be selected well and the noisy point sets will be smoothed. The interpolation points are renewed by clustering method, and implicit reconstruction by CSRBF may provide a solution for surface reconstruction from noisy and incomplete data. In our algorithm, we represent the point sets by an octree-based subdivision of the bounding box to reduce computation complexity

This paper presents an image segmentation method based on fuzzy clustering and level set methods. The MRI image is firstly segmented with the fuzzy clustering method, and then the resulting fuzzy memberships are used to generate a new constraint function which guides the level-set curves evolution. Thanks to the new constraint term, the presented method is able to adaptively determine the directions of the curves evolution without any manual intervention. The new method does not depend excessively on the gradient information as the Geodesic Active Contour method does; therefore it is less sensitive to the noise. The region information provided by the first step of the algorithm allows to improve the segmentation accuracy of the brain tissues. The experimental quantitative and qualitative analyses indicate the capability of the proposed method to segment the brain tissues with high accuracy comparing with the fuzzy clustering method.

In order to improve the capability of enterprise economic risk assessment and prediction, this paper combines panel data analysis method to construct enterprise economic risk prediction model, proposes enterprise economic risk prediction model based on big data fusion, establishes fusion clustering model of enterprise economic risk assessment panel data, and uses fuzzy correlation fusion clustering method to make statistical analysis of enterprise economic risk assessment panel data. Quantitative regression analysis and fuzzy clustering methods are used for panel data mining of enterprise economic risk assessment, adaptive block feature matching in the risk prediction process is carried out according to the mining results of panel data bureau, interference suppression in the enterprise economic risk prediction process is carried out by using correlation feature matching method, large data fusion in the enterprise economic risk prediction process is carried out by combining fuzzy information clustering and statistical analysis methods, and optimization of enterprise economic risk prediction is realized according to the rule distribution relation of recursive entropy. The simulation results show that the method has high accuracy in predicting the economic risks of enterprises, improves the data mining and information fusion capabilities of enterprises' economic risks, and improves the anti-risk capability of enterprises.

Economic growth is the process of increasing a country's capacity to provide goods and services to its population, which is an important indicator of the success of a region's development. The Fuzzy C-Means method was used to group provincial data in Indonesia based on economic growth in 2023 because of the ability of this technique to handle membership uncertainties that often occur. This research aims to understand the economic inequality that exists in various provinces, as well as provide useful information for the government in formulating more effective policies. The data used in this research is secondary data from the Central Statistics Agency, covering 34 provinces with important indicators in economic growth totaling 9 variables. The results of the analysis show that provinces in Indonesia can be grouped into two main clusters. The first cluster consists of provinces with relatively high economic growth, while the second cluster includes provinces with lower economic growth and higher levels of inequality. It is hoped that this research can provide a clear picture of the distribution of economic growth between provinces in Indonesia and assist the government in formulating appropriate strategies to improve economic prosperity evenly.

Bias-correction fuzzy clustering algorithms

Group technology’s basic logic is grouping and producing products of the same type together. An important reason behind Group Technology becoming such an important topic is that nowadays companies have quite an extensive range and workshop type production has increased. Both fuzzy clustering and rank order clustering methods use for grouping parts and machines based on a part-machine matrix created from the production flow technique in order to increase productivity and reduce cost and workmanship required. In this study, Group Technology techniques such as the rank order clustering and fuzzy clustering methods were applied in order to increase the efficiency of the production line, reduce transportation between machines, and form a machine-parts groups in the wood cutting department of a furniture company producing modular furniture in Istanbul. The TOPSIS method was used to determine which products to take into account. According to results of the study, it is shown that fuzzy clustering method has overperformed rank order clustering method based on the evaluation criteria which are group productivity with 21,36%, group efficiency with 43,21% and grouping measure with 82,33%.

Fuzzy Clustering is applied to the problem of detecting landmines in Ground Penetrating Radar (GPR). The DARPA Backgrounds Data provides a rich source of signatures derived from a cluttered environment with a variety of sensors. One sensor used in the Backgrounds collection was the GPR developed and fielded by Geo-Centers, Inc. This GPR provides a three-dimensional array of intensity returns corresponding to a volume underneath the ground. In this paper, a novel approach to processing that GPR is described. The approach relies on computing edge direction and magnitude features in the volume and comparing them to prototypes generated using fuzzy c-means clustering. A confidence map is generated corresponding to the surface traversed by the system. The confidence map is thresholded to produce detections. Experimental results show a reduction in false alarm rates from about 40% using the standard processing method to about 4% using the three-dimensional, fuzzy clustering method.

Ecosystem discrimination and fingerprinting of Romanian propolis by hierarchical fuzzy clustering and image analysis of TLC patterns

The rolling bearings are considered as the heart of rotating machinery and early fault diagnosis is one of the biggest challenges during operation. Due to complicated mechanical assemblies, detection of the advancing fault and faults at the incipient stage is very difficult and tedious. This work presents a fuzzy rule based classification of bearing faults using Fuzzy C-means clustering method using vibration measurements. Experiments were conducted to collect the vibration signals of a normal bearing and bearings with faults in the inner race, outer race and ball fault. Discrete Wavelet Transform (DWT) technique is used to decompose the vibration signals into different frequency bands. In order to detect the early faults in the bearings, various statistical features were extracted from this decomposed signal of each frequency band. Based on the extracted features, Fuzzy C-means clustering method (FCM) is developed to classify the faults using suitable membership functions and fuzzy rule base is developed for each class of the bearing fault using labeled data. The experimental results show that the proposed method is able to classify the condition of the bearing using the extracted features. The proposed FCM based clustering and classification model provides easier interpretation and implementation for monitoring the condition of the rolling bearings at an early stage and it will be helpful to take the preventive action before a large-scale failure.