The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
The random Fourier Features method has been found very effective in approximating the kernel functions. Our former studies show that through a mixing mechanism of the feature space formed by random Fourier features and certain linear algorithms, the fuzzy clustering results in the approximated feature space are comparable to or even exceed the classical kernel-based algorithms. To increase the robustness...
Data explosion drives data analysis tools to update faster and faster, while clustering plays an indispensable role in knowledge discovery. Whereas, most of the clustering algorithms only effect on those linear separable data. Kernel-based clustering methods perform well on data sets with non-linear inner structure, but at the same time, the requirement of large memory and running time induce poor...
Although fuzzy c-means algorithm has shown great capability to spherical clusters, it can not perform very well on non-spherical data sets yet. To deal with this problem, kernel-based fuzzy clustering has been presented by mapping data points into a high-dimensional Hilbert space with kernel functions. However, the computational complexity of kernel matrix is always quadratic, usually makes kernel...
This paper applies fuzzy clustering algorithm to recognize the transformer winding's pressed state based on transformer's vibration signal. We propose a new semi-supervised fuzzy kernel clustering algorithm (SFKC) based on some modifications for the fuzzy clustering methods. The first modification is that the new algorithm uses prior knowledge to guide the clustering process. Second, it uses kernel...
In order to apply successfully the fuzzy clustering algorithms like shadowed C-means (SCM) to image segmentation problems, the spatial information related with each pixel in the image should be carefully calculated and appended to the clustering algorithms. In this paper, the non-local spatial information calculation is introduced to SCM. Because the data in the kernel space demonstrate more linearly-separable...
Soft subspace fuzzy clustering algorithms have been successfully utilized for high dimensional data in recent studies. However, the existing works often utilize only one distance function to evaluate the similarity between data items along with each feature, which leads to performance degradation for some complex data sets. In this work, a novel soft subspace fuzzy clustering algorithm MKEWFC-K is...
This paper presents two incremental clustering algorithms based on FCMK, a fuzzy clustering with multiple kernels algorithm we developed earlier [1]. The FCMK algorithm has a memory requirement of O(N2), where N is the number of objects in the data set. Thus, even data sets that have nearly 1, 000, 000 objects require terabytes of working memory-impractical for most computers. One way to attack this...
A new shadowed c-means clustering based image segmentation method is proposed in this paper. By including the local spatial information in shadowed c-means algorithm and mapping the original data into a high dimensional space via kernel method, we propose the Kernel Spatial Shadowed C-Means (KSSCM) clustering algorithm for image segmentation problems. The KSSCM based approach shows better performance...
Proximity-based fuzzy c-means algorithm (P-FCM), a classical semi-supervised clustering algorithm, concerns with the number of proximity “hints” or constraints that specify an extent to which some pairs of instances are considered similar or. By replacing the fuzzy c-means in P-FCM with a kernel fuzzy c-means, this paper proposes a new semi-supervised clustering algorithm named proximity-based kernel...
While classical kernel-based clustering algorithms are based on a single kernel, in practice it is often desirable to base clustering on combination of multiple kernels. In [1], we considered a fuzzy c-means with multiple kernels in observation space (FCMK-OS) algorithm which constructs the kernel from a number of Gaussian kernels and learns a resolution specific weight for each kernel function in...
This paper presents cluster validity for kernel fuzzy clustering. First, we describe existing cluster validity indices that can be directly applied to partitions obtained by kernel fuzzy clustering algorithms. Second, we show how validity indices that take dissimilarity (or relational) data D as input can be applied to kernel fuzzy clustering. Third, we present four propositions that allow other existing...
Intuitionistic Fuzzy C-means (IFCM) is a robust clustering method which is based upon intuitionistic fuzzy set theory. It uses Euclidean distance as a distance metric, hence can only cluster hyper spherically distributed data-sets in data space or in feature space. FCM and KFCM with a new distance measure (FCM-σ and KFCM-σ) can detect non-hyperspherical clusters in data space and feature space but...
We propose a new relational clustering approach, called Fuzzy clustering with Learnable Cluster dependent Kernels (FLeCK), that learns multiple kernels while seeking compact clusters. A Gaussian kernel is learned with respect to each cluster. It reflects the relative density, size, and position of the cluster with respect to the other clusters. These kernels are learned by optimizing both the intra-cluster...
The detection of pulmonary nodules is one of the most studied areas and challenging task in the field of medical image analysis, due the current relevance of the lung carcinoma. The difficulty and complexity of this task has led to the development of CAD systems for the automated detection of lung nodules in CT scans, which provides valuable assistance for radiologists and could improve the detection...
In this paper, the kernel fuzzy c-means clustering algorithm is extended to an adaptive cluster model which maps data points to a high dimensional feature space through an optimal convex combination of homogenous kernels with respect to each cluster. This generalized model, called Fuzzy C-Means with Multiple Kernels (FCM-MK), strives to find a good partitioning of the data into meaningful clusters...
Aiming at sensitivity of noise on WSVDD and circumstance of can not be separated in multi-classification problem, the paper presented a multi-classification method based on fuzzy weighted support vector description algorithm. Inspired by weighed SVDD, the method assigned weight to each training sample to build super-ball, while its weight does not take into account the effect of characteristics of...
Fuzzy Fisher Criterion(FFC) based clustering method uses the fuzzy Fisher's linear discriminant(FLD) as its clustering objective function and is more robust to noises and outliers than fuzzy c-means clustering(FCM). But FFC can only be used in linear separable dataset. In this paper, a novel fuzzy clustering algorithm, called Kernelized Fuzzy Fisher Criterion(KFFC) based clustering algorithm, is proposed...
In this paper, a novel learning method based on kernelized fuzzy clustering and least squares support vector machines (LSSVM) is presented to improve the generalization ability of a Takagi-Sugeno-Kang (TSK) fuzzy modeling. Firstly, the fuzzy partition of the product space of input and output is obtained by kernelized fuzzy clustering. Then, a computationally efficient numerical method is proposed...
Takagi-Sugeno models are an important class of fuzzy rule based oriented models, generally used for prediction and control. Fuzzy clustering is one of effective methods for identification. In this method, we propose to use a fuzzy clustering method (Kernel based fuzzy c-means method) for automatically constructing a multi-input fuzzy model to identify the structure of a fuzzy model. To clarify the...
In order to improve the accuracy of multi-spectra remote sensing image classification, a terrain classification method based on support vector machine is proposed. A remote sensing image classification method based on SVM algorithm of C-SVC type is introduced and emphasis is put on the study of the improved SMO algorithm. In order to improve efficiency of classification, multiple-spectra remote sensing...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.