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Clustering is an effective method for data analysis and can be exploited to unknown features of data samples, its applications range from data mining to bioinformatics analysis. Several clustering approaches have been proposed in order to obtain a better trade-off between accuracy and efficiency of the clustering process. It is well-known that no existing clustering algorithm completely satisfies...
We propose a method for semi-supervised classification using a combination of ensemble clustering and kernel based learning. The method works in two steps. In the first step, a number of variants of clustering partition are obtained with some clustering algorithm working on both labeled and unlabeled data. Weighted averaged co-association matrix is calculated using the results of partitioning. We...
Aiming at the multiple attribute decision making problem with three-parameter interval grey numbers, a grey-incidence clustering decision making method based on regret theory is proposed in this paper. First, according to the idea of TOPSIS method, a kind of comprehensive grey interval incidence coefficient of three-parameter interval grey number is defined, and the “regret-rejoice” value is calculated...
Fuzzy clustering has emerged as an important tool for discovering the structure of data. Kernel based clustering has emerged as an interesting and quite visible alternative in fuzzy clustering. Aimed at the problems of both a local optimum and depending on initialization strongly in the fuzzy c-means clustering algorithm (FCM), a method of kernel-based fuzzy c-means clustering based on fruit fly algorithms...
Data clustering methods have been used extensively for image segmentation in the past decade. In our previous work, we had established that combining the traditional clustering algorithms with a meta-heuristic like Firefly Algorithm improves the stability of the output as well as the speed of convergence. In this paper, we have replaced the Euclidean distance formula with kernels. We have combined...
Automatic discrimination of big time series trends is researched in this paper. We focus on the three steps of traditional spectral clustering, that are similarity matrix construction, eigenvalue decomposition and eigenvector selection, and K-means clustering of the selected eigenvector, an algorithm based on MapReduce framework is designed. In order to achieve the goal for automatic identification...
In this paper we present the comparative study of different bifurcation analysis techniques for nonlinear dynamical systems research. The study is given through a comparison of algorithms for multiparametric diagram plotting. Thomas chaotic attractor is considered as a test dynamical system. The paper reviews statistical histogram algorithm, the kernel density estimation (KDE) algorithm and classical...
Based on the local density clustering (LDC) algorithm, a new automatical local density clustering (ALDC) algorithm is proposed in this paper. Different from the existing LDC algorithm, the ALDC can capture the cluster center automatically. The new algorithm calculates the local density and the distance deviation of every point and expands the difference between the potential cluster center and other...
The density peak based clustering algorithm is a recently proposed clustering approach. It uses the local density of each data and the distance to the nearest neighbor with higher density to isolate and identify the cluster centers. After the cluster centers are identified, the other data are assigned labels equaling to those of their nearest neighbors with higher density. This algorithm is simple...
The density peak based clustering algorithm is a simple yet effective clustering approach. This algorithm firstly calculates the local density of each data and the distance to the nearest neighbor with higher density. Based on the assumption that cluster centers are density peaks and they are relatively far from each other, this algorithm isolates the candidates of cluster centers from the non-center...
In clustering applications, multiple views of the data are often available. Although clustering could be done within each view independently, exploiting information across views is promising to gain clustering accuracy improvement. A common assumption in the field of multi-view learning is that the clustering results from multiple views should be consistent with a latent clustering. However, the potential...
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...
Because the contrast of the image for guiding the high-speed infrared air-to-air missile is low, its signal to noise ratio is poor and the target and its background gray-scale coupling is strong, the paper analyzes the reasons why the threshold value segmentation method and the fuzzy C-means clustering method have the over-segmentation and under-segmentation in segmenting the above type of image....
We address the problem of how to design a more effective co-training scheme to tackle the multi-view spectral clustering. The conventional co-training procedure treats information from all views equally and often converges to a compromised consensus view that does not fully utilize the multiview information. We instead propose to learn an augmented view and construct its corresponding affinity matrix...
As the K-means algorithm is dependent on the initial clustering center, and the particle swarm optimization (PSO) converges prematurely and is easily trapped in local minima, a Gaussian kernel particle swarm optimization clustering algorithm is proposed in this paper. The algorithm adopts the theory of good point set to initialize population, which makes the initial clustering center more rational...
In this study, we propose three new algorithms based on difference of convex (DC) programming and DC algorithm (DCA) for kernel fuzzy c-means (KFCM) clustering model. Firstly, KFCM model is reformulated into two equivalent forms of DC programmings for which different KFCM algorithms are designed. Then, to further accelerate the second DCA based KFCM algorithm, we adopt an approximate strategy which...
A new algorithm for apple disease image segmentation is proposed. A fuzzy factor for weighted balance is introduced in the algorithm to describe the coefficient of spatial constraints between pixels in neighborhood. For enhancing the integrality of neighbor information, the space distance constraints and the spatial gray constraints are considered. The fuzzy factor in the neighborhood is used to keep...
As the symbol of the partition clustering method, K-Means is well known and widely used in many fields for the easily implemented and high efficiency. However, the initial center problem may affect the final cluster result, sometimes the final cluster result might contain some empty clusters. In this paper, a new K-Mean initialization method is proposed which combines the statistical information and...
Feature selection is an effective technique for dimensionality reduction to get the most useful information from huge raw data. Many spectral feature selection algorithms have been proposed to address the unsupervised feature selection problem, but most of them fail to pay attention to the noises induced during the feature selection process. In this paper, we not only consider the feature structural...
Herein, we explore both a new supervised and unsupervised technique for dimensionality reduction or multispectral sensor design via band group selection in hyperspectral imaging. Specifically, we investigate two algorithms, one based on the improved visual assessment of clustering tendency (iVAT) and the other based on the automatic extraction of “blocklike” structure in a dissimilarity matrix (CLODD...
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