Fuzzy C-means Algorithm Algorithm Research Articles

A modified fuzzy clustering approach in unsupervised classification for detecting the mixed pixels of satellite images

The major problem of remote sensing images is mixed pixels, available in the data which degrades the quality, accuracy of the image classification and object recognition. To overcome the problem of mixed pixel in a real satellite data a modified K -means clustering algorithm and a modified fuzzy C -means clustering algorithm, are discussed. The algorithms are developed by modifying the membership function of the standard K -means clustering algorithm (FKM) and the standard fuzzy C -means algorithm (FCM). The performance of the proposed algorithms is discussed and compared with the traditional fuzzy K -means algorithm and the traditional FCM algorithm. Results on classification and segmentation of satellite images reveal that the suggestive algorithms are robust and effective.

Health Monitoring of Landing Gear Retraction/Extension System Based on Optimized Fuzzy C-Means Algorithm

The landing gear retraction/extension(R/E) system has a critical impact on the safety of aircraft’s take-off and landing, and its health status is important to decision-making of the system’s prognostics and health management (PHM). With flight parameter data, a method to monitor the health of aircraft landing gear R/E system is proposed based on the improved fuzzy c-means algorithm (FCM). The landing gear health status are classified by the FCM cluster, and the granularity principle and density function are utilized to optimize the number of FCM algorithm clusters and the initial clustering center to better classification. At the same time, an optimized multi-dimensional scaling algorithm (MDS) is used to obtain low-dimensional features which facilitate cluster analysis. And the algorithm comparison and case analysis results show that the proposed method has a great health monitoring effect.

A fuzzy c-means algorithm based on the relationship among attributes of data and its application in tunnel boring machine

In recent years, a number of operation data from engineering systems have been measured and recorded, which promotes the development of engineering data mining. However, the operating state of the engineering system usually changes greatly, which results that the patterns of operation data vary considerably as well. Thus, partitioning these data can provide useful references to the design and analysis of engineering systems. In this paper, a new clustering algorithm based on support vector regression and fuzzy c-means algorithm (SVR–FCM) is proposed to accomplish this work. The SVR–FCM algorithm is based on the framework of fuzzy c-means algorithm (FCM), in which the differences between the clusters are evaluated by the relationship among attributes of data. In the proposed algorithm, support vector regression (SVR) is utilized to describe the relationship among attributes of, and an alteration optimization method is designed to optimize the new designed clustering objective function. A series of experiments on synthetic datasets and real-world datasets are conducted to evaluate the performance of the SVR–FCM algorithm, which shows the higher effectiveness and advances of the SVR–FCM algorithm compared with other popular clustering algorithms. The SVR–FCM algorithm is applied to a tunnel boring machine (TBM) operation dataset collected from a real TBM project in China. The experimental results show that the proposed algorithm performs well in TBM operation data clustering. This paper also highlights the applicability and potential of data clustering in the analysis of other complex engineering systems similar to TBMs.

A fuzzy data reduction cluster method based on boundary information for large datasets

The fuzzy c-means algorithm (FCM) is aimed at computing the membership degree of each data point to its corresponding cluster center. This computation needs to calculate the distance matrix between the cluster center and the data point. The main bottleneck of the FCM algorithm is the computing of the membership matrix for all data points. This work presents a new clustering method, the bdrFCM (boundary data reduction fuzzy c-means). Our algorithm is based on the original FCM proposal, adapted to detect and remove the boundary regions of clusters. Our implementation efforts are directed in two aspects: processing large datasets in less time and reducing the data volume, maintaining the quality of the clusters. A significant volume of real data application (> 106 records) was used, and we identified that bdrFCM implementation has good scalability to handle datasets with millions of data points.

To Explore Intracerebral Hematoma with a Hybrid Approach and Combination of Discriminative Factors.

To find discriminative combination of influential factors of Intracerebral hematoma (ICH) to cluster ICH patients with similar features to explore relationship among influential factors and 30-day mortality of ICH. The data of ICH patients are collected. We use a decision tree to find discriminative combination of the influential factors. We cluster ICH patients with similar features using Fuzzy C-means algorithm (FCM) to construct a support vector machine (SVM) for each cluster to build a multi-SVM classifier. Finally, we designate each testing data into its appropriate cluster and apply the corresponding SVM classifier of the cluster to explore the relationship among impact factors and 30-day mortality. The two influential factors chosen to split the decision tree are Glasgow coma scale (GCS) score and Hematoma size. FCM algorithm finds three centroids, one for high danger group, one for middle danger group, and the other for low danger group. The proposed approach outperforms benchmark experiments without FCM algorithm to cluster training data. It is appropriate to construct a classifier for each cluster with similar features. The combination of factors with significant discrimination as input variables should outperform that with only single discriminative factor as input variable.

A Self-Adaptive Fuzzy c-Means Algorithm for Determining the Optimal Number of Clusters.

For the shortcoming of fuzzy c-means algorithm (FCM) needing to know the number of clusters in advance, this paper proposed a new self-adaptive method to determine the optimal number of clusters. Firstly, a density-based algorithm was put forward. The algorithm, according to the characteristics of the dataset, automatically determined the possible maximum number of clusters instead of using the empirical rule and obtained the optimal initial cluster centroids, improving the limitation of FCM that randomly selected cluster centroids lead the convergence result to the local minimum. Secondly, this paper, by introducing a penalty function, proposed a new fuzzy clustering validity index based on fuzzy compactness and separation, which ensured that when the number of clusters verged on that of objects in the dataset, the value of clustering validity index did not monotonically decrease and was close to zero, so that the optimal number of clusters lost robustness and decision function. Then, based on these studies, a self-adaptive FCM algorithm was put forward to estimate the optimal number of clusters by the iterative trial-and-error process. At last, experiments were done on the UCI, KDD Cup 1999, and synthetic datasets, which showed that the method not only effectively determined the optimal number of clusters, but also reduced the iteration of FCM with the stable clustering result.

A Detection Method for Transmission Line Insulators Based on an Improved FCM Algorithm

An improved segmentation Fuzzy C-Means algorithm (FCM) is proposed for the image recognition of transmission line insulators. In this paper, the improved Wiener filter algorithm is firstly used to filtrate and recover image in pre-processing. Then, the insulator image is segmented based on the improved algorithm FCM. Finally, the contour of insulator is labelled by using connected component labelling algorithm. Experimental results have shown that the improved Wiener filtering algorithm may effectively filter and recover the images; furthermore, the improved FCM image segmentation algorithm may accurately segment insulators from the image.

Implementation of the Fuzzy C-Means Clustering Algorithm in Meteorological Data

An improved fuzzy c-means algorithm is put forward and applied to deal with meteorological data on top of the traditional fuzzy c-means algorithm. The proposed algorithm improves the classical fuzzy c-means algorithm (FCM) by adopting a novel strategy for selecting the initial cluster centers, to solve the problem that the traditional fuzzy c-means (FCM) clustering algorithm has difficulty in selecting the initial cluster centers. Furthermore, this paper introduces the features and the mining process of the open source data mining platform WEKA, while it doesn’t implement the FCM algorithm. Considering this shortcoming of WEKA, we successfully implement the FCM algorithm and the advanced FCM algorithm taking advantage of the basic classes in WEKA. Finally, the experimental clustering results of meteorological data are given, which can exactly prove that our proposed algorithm will generate better clustering results than those of the K-Means algorithm and the traditional FCM algorithm.

Breast density quantification with cone-beam CT: a post-mortem study

Forty post-mortem breasts were imaged with a flat-panel based cone-beam x-ray CT system at 50 kVp. The feasibility of breast density quantification has been investigated using standard histogram thresholding and an automatic segmentation method based on the fuzzy c-means algorithm (FCM). The breasts were chemically decomposed into water, lipid, and protein immediately after image acquisition was completed. The per cent fibroglandular volume (%FGV) from chemical analysis was used as the gold standard for breast density comparison. Both image-based segmentation techniques showed good precision in breast density quantification with high linear coefficients between the right and left breast of each pair. When comparing with the gold standard using %FGV from chemical analysis, Pearson's r-values were estimated to be 0.983 and 0.968 for the FCM clustering and the histogram thresholding techniques, respectively. The standard error of the estimate was also reduced from 3.92% to 2.45% by applying the automatic clustering technique. The results of the postmortem study suggested that breast tissue can be characterized in terms of water, lipid and protein contents with high accuracy by using chemical analysis, which offers a gold standard for breast density studies comparing different techniques. In the investigated image segmentation techniques, the FCM algorithm had high precision and accuracy in breast density quantification. In comparison to conventional histogram thresholding, it was more efficient and reduced inter-observer variation.

Non-uniform illumination correction in infrared images based on a modified fuzzy c-means algorithm

The correction of non-uniform illumination and the elimination of shading artifacts is an important preprocessing task used in a great number of image processing applications such as segmentation, registration or quantitative analysis. Although, a careful and accurate set up of the image acquisition system may degrade the importance of a brightness normalization algorithm, non-uniform illumination appears due to the interaction of objects and light on the scene requires retrospective shading correction. The image formation process and the corresponding shading effects are described by a linear image formation model, which consists of a multiplicative and an additive shading component. In this paper a novel brightness normalization method is proposed to eliminate the non-uniform illumination effects. The method is based on the application of a fuzzy c-means algorithm (FCM) only on the background part of the acquired image, where the objective function is modified to take into account local information of each pixel in the estimation of the multiplicative and the additive shading components. The modified FCM algorithm is iterative, as the standard FCM, and at each iteration the multiplicative and the additive shading components are re-estimated based on the cluster centers and the membership of each pixel in a specific cluster. Brightness correction is performed by the inverse of the image formation model after FCM convergence. Experiments were conducted in a database of both real and artificial infrared images. The experimental results show that the proposed method decreases significantly the non-uniform illumination effects and does not introduce brightness variations if the background is uniform.

Fault Diagnosis Based on Fuzzy C-means Algorithm of the Optimal Number of Clusters and Probabilistic Neural Network

Fault diagnosis is essential for the reliable, safe, and efficient operation of the plant and for maintaining quality of the products in industrial system. This paper presents an ensemble fault diagnosis algorithm based on fuzzy c-means algorithm (FCM) with the Optimal Number of Clusters (ONC) and probabilistic neural network (PNN), called FCM-ONC-PNN. In clustering methods, the estimation of the optimal number of clusters is significant for subsequent analysis. As a simple clustering method, FCM has been widely discussed and applied in pattern recognition and machine learning, but FCM could not guarantee unique clustering result because initial cluster number is chosen randomly. As the number of clusters is randomly chosen, the iterative amount is large and the result of the classification is unstable. In this paper, firstly subtractive clustering is proposed to find the optimal number of clusters and the clustering results of the FCM are compared with random initialization method, and then PNN is used to classify the clustering data of FCM. The experiments show that the modified initial cluster number of FCM algorithm can improve the speed, and reduce the iterative amount. At the same time, FCM-ONC-PNN approach can make classification more stable and have higher precision.

SEMIAUTOMATIC DETECTION OF TUMORAL ZONE

This paper describes a robust method based on the cooperation of fuzzy classification and regions segmentation algorithms, in order to detect the tumoral zone in the brain Magnetic Resonance Imaging (MRI). On one hand, the classification in fuzzy sets is done by the Fuzzy C-Means algorithm (FCM), where a study of its different parameters and its complexity has been previously realised, which led us to improve it. On the other hand, the segmentation in regions is obtained by an hierarchical method through adaptive thresholding. Then, an operator expert selects a germ in the tumoral zone, and the class containing the sick zone is localised in return for the FCM algorithm. Finally, the superposition of the two partitions of the image will determine the sick zone. The originality of our approach is the parallel exploitation of different types of information in the image by the cooperation of two complementary approaches. This allows us to carry out a pertinent approach for the detection of sick zone in MRI images.