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Clustering results are often affected by covariates that are independent of the clusters one would like to discover. Traditionally, Alternative Clustering algorithms can be used to solve such a problem. However, these suffer from at least one of the following problems: i) continuous covariates or non-linearly separable clusters cannot be handled; ii) assumptions are made about the distribution of...
Matrix factorization is a popular low dimensional representation approach that plays an important role in many pattern recognition and computer vision domains. Among them, convex and semi-nonnegative matrix factorizations have attracted considerable interest, owing to its clustering interpretation. On the other hand, the generalized correlation function (correntropy) as the error measure does not...
In this paper, we propose a novel Latent Multi-view Subspace Clustering (LMSC) method, which clusters data points with latent representation and simultaneously explores underlying complementary information from multiple views. Unlike most existing single view subspace clustering methods that reconstruct data points using original features, our method seeks the underlying latent representation and...
Image segmentation is an important problem in image processing and object recognition, and is one well-known bottleneck for further applications. Fuzzy C-means, as one typical clustering algorithm in pattern recognition, has been improved for image segmentation in many aspects. Aiming at the distance form in FCM, this paper proposes to incorporate FCM with kernel functions, which will make it insensitive...
Infrared polarization imaging detection can be used to obtain not only the polarization state but also the radiation of target. With this method, the target that traditional photometry cannot detect can be settled. The degree and angle of polarization that used in polarization detection reflect different physical properties, and it is seriously redundancy along with intensity of images. A target detection...
Estimation of bias field together with the tissue class of a noisy Magnetic Resonance image has been a challenging task because of the nonlinear nature of bias field. In order to address this issue we have proposed two new schemes. The first one is the recursive framework, where class labels and bias fields have been estimated simultaneously. In one part of the recursion, a variable variance Adaptive...
Non-negative Matrix Factorization (NMF) has been widely studied and applied to variant computer vision tasks, such as image clustering and pattern classification. Meanwhile, real world stimuli for human neural system (e.g., face images) are usually represented as high-dimensional data vectors rely on graph embedding in original Euclidean space. Thus, the traditional NMF and its variants exhibit weakness...
Differences in treatment of gliomatosis cerebri and brain infection are crucial to the healing process. Nowadays, Magnetic Resonance Spectroscopy (MRS) is used to determine the content of metabolites in patients with glioma (astrocytoma) or brain infection. An analysis of the MRS cannot be used as a reference for determining whether a patient suffering from brain glioma or brain infection. This paper...
The disadvantages of BOW (Bag of words model) for image classification include the large amount of data in generating a codebook by clustering, redundant code words that may affect the classification results and so on. The process of BOW for the classification can be improved through the Laplace weights to improved fuzzy C means algorithm, and obtaining codebook with more ability to distinguish between...
Clustering algorithm is often used to analyze the communication data for network intrusion detection system. However, network communication data are mixed, e.g., numerical and categorical data. So, at first, this paper put forward a method for representing the cluster center (prototype) of mixed-type data. Then respectively in combination with the continuity characteristic of the numerical attributes...
In fuzzy clustering algorithm, fuzzy possibilistic C-means clustering algorithm (FPCM) is widely used. However, the method is sensitive to its parameters and the clustering accuracy and robustness is poor. In order to overcome the above problems, this paper presents an intuitionistic fuzzy possibilistic C-means clustering based on genetic algorithm (IFPCM-GA). IFPCM-GA does not only retain the advantages...
This paper presents a survey of Hybrid fuzzy c-means (FCM) clustering algorithms, The algorithmic steps, parameters involved in the algorithm & the experimental results on various datasets of several hybrid clustering methods are discussed in this paper. Hybrid FCM clustering techniques are obtained by modifying the FCM either by incorporating hesitation degree of Intuionistic approach or by replacing...
The label tree-based classification is one of the most popular approaches for reducing the testing complexity to sublinear with the large number of classes. One of the popular approaches to generate the label tree is to apply recursively a spectral clustering algorithm to an affinity matrix for partition set of class labels into subsets, each subset corresponds to a child node of the tree. To obtain...
In this paper, we adapt two existing methods to perform semi-supervised temporal clustering: Aligned Cluster Analysis (ACA), a temporal clustering algorithm, and Constrained Spectral Clustering, a semi-supervised clustering algorithm. In the first method, we add side information in the form of pair wise constraints to its objective function, and in the second, we add a temporal search to its framework...
Accurate segmentation of breast on MR images is an essential and crucial step for computer-aided breast disease diagnosis and surgical planning. In this paper, an effective approach is proposed for segmenting the breast image into different regions, each corresponding to a different tissue. The segmentation work flow comprises two key steps. Firstly, we use the threshold-based method and morphological...
This study is concerned with text clustering and evaluating the clusters. Fuzzy neighborhood, a method of text mining, is used for this purpose. Five different clustering algorithms are used, they are kernel affinity propagation, kernel hard c-means, kernel fuzzy c-means, kernel hard k-means++ and kernel variable size hard c-means. These algorithms are applied to the clustering of nouns and adjectives...
In this study, two clustering frameworks are proposed based on a maximizing model of spherical Bezdek-type fuzzy clustering are proposed. One using possibilistic c-means, and the other using multi-medoids. In each framework, the basic model and its kernelization are presented, along with an appropriate spectral clustering technique. Kernelization allows the frameworks to capture nonlinear-bordered...
This paper presents a new sequential clustering algorithm based on sequential hard c-means clustering. The word sequential cluster extraction means that the algorithm extract one cluster at a time. The sequential hard c-means is one of the typical and conventional sequential clustering methods. The proposed new sequential clustering algorithm is based on Dave's noise clustering approach. A characteristic...
Fuzzy clustering techniques, especially Fuzzy C-Means clustering method (FCM), is a popular algorithm widely used in the images segmentation. However, as the conventional FCM doesn't optimize data in feature space and doesn't involve any spatial information, it is sensitive to the noise. In the paper, we presented a novel FCM clustering algorithm based on kernel spatial information to segment the...
For a given data set, exploring their multi-view instances under a clustering framework is a practical way to boost the clustering performance. This is because that each view might reflect partial information for the existing data. Furthermore, due to the noise and other impact factors, exploring these instances from different views will enhance the mining of the real structure and feature information...
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