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Based on fuzzy C-means method and the characteristics of kernel-based method, the algorithm of kernel-based fuzzy clustering is presented, in which the objective function of fuzzy C-means is substituted by Gaussian kernel objective function. The approach of kernel-based fuzzy C-means clustering is used in the classification and recognition of remote sensing images, and the result shows that it can...
Bhattacharrya distance (BD) is a widely used distance in statistics to compare probability density functions (PDFs). It has shown strong statistical properties (in terms of Bayes error) and it relates to Fisher information. It has also practical advantages, since it strongly relates on measuring the overlap of the supports of the PDFs. Unfortunately, even with common parametric models on PDFs, few...
In recent years bag-of-visual-words representations have gained increasing popularity in the field of image classification. Their performance highly relies on creating a good visual vocabulary from a set of image features (e.g. SIFT). For real-world photo archives such as Flicker, codebooks with larger than a few thousand words are desirable, which is infeasible by the standard k-means clustering...
The conventional mean shift algorithm has been known to be sensitive to selecting a bandwidth. We present a robust mean shift algorithm with heterogeneous node weights that come from a geometric structure of a given data set. Before running MS procedure, we reconstruct un-normalized weights (a rough surface of data points) from the Delaunay Triangulation. The un-normalized weights help MS to avoid...
In this paper, we present a scalable evolutionary algorithm for clustering large and dynamic data sets, called Scalable Evolutionary Clustering with Self Adaptive Genetic Operators (Scalable ECSAGO). The proposed evolutionary clustering algorithm can adapt its genetic operators rate while the evolution leads to the optimal centers of the clusters. The sizes of the clusters are estimated using a hybrid...
Recently semi-supervised clustering has been studied by many researchers, but there are no extensive studies using different types of algorithms. In this paper we consider agglomerative hierarchical algorithms with pairwise constraints. The constraints are directly introduced to the single linkage which is equivalent to the transitive closure algorithm, while the centroid method and the Ward methods...
This paper considers cluster validation for fuzzy clustering with noise rejection. Although noise rejection mechanisms such as noise fuzzy clustering or graded possibilistic noise rejection make it possible to remove the influence of noisy samples, they also create problems in applying conventional validity measures designed for fuzzy clustering with probabilistic constraints. In this paper, a PCA-guided...
Support vector machine (SVM) is an effective method for resolving regression problem. However, tradition SVM is very sensitive to noises in the training sample. In order to overcome this problem, fuzzy support vector regression (FSVR) based on combining cluster center with affinity is proposed in this paper. The fuzzy membership is defined not only by the distance between a point and its cluster center,...
Fuzzy c-means (FCM) is a simple but powerful clustering method using the concept of fuzzy sets that has been proved to be useful in many areas. There are, however, several well known problems with FCM, such as sensitivity to initialization, sensitivity to outliers, and limitation to convex clusters. In this paper, global fuzzy c-means (G-FCM) and kernel fuzzy c-means (K-FCM) are combined and extended...
An explicit mapping is generally unknown for kernel data analysis but their inner product should be known. Though kernel fuzzy c-means algorithm for data with tolerance has been proposed by the authors, the cluster centers and the tolerance in higher dimensional space have been unseen. Contrary to this common assumption, an explicit mapping has been introduced by one of the authors and the situation...
Because of the compactness characteristics of oil-immersed transformer fault, a model of oil-immersed transformer fault diagnosis is based on the collaborative method of Fuzzy Kernel C-Means Clustering (FKCM) and multi-source information data fusion. The basic idea is that the trained samples are clustered first by using FKCM, then a Dempster-Shafer(D-S) evidential theory Fusion method is used to...
A key question in medical decision support is how best to visualise a patient database, with especial reference to cohort labelling, whether this is an indicator function for classification or a cluster index. We propose the use of the kernel trick to visualise complete patient databases, in low-dimensional projections, with class labelling, given a non-linear classifier of choice. The results show...
While data clustering has a long history and a large amount of research has been devoted to the development of clustering algorithms, significant challenges still remain. One of the most important challenges in the field is dealing with high dimensional datasets. The class of clustering algorithms that utilises information from Principal Component Analysis has proven very successful in such datasets...
With the repaid development of internet technology, image documents have become an important information source. It is hard for us to retrieve certain images from all available ones. In this paper, we propose an interactive image recommendation system, which firstly uses color histogram feature or Gabor texture feature to express image contents, then a kernel based K-meanse is utilized to cluster...
Image segmentation and unsupervised classification are difficult problems. We propose to combine both. A clustering process is applied over segment mean values. Only large segments are considered. The clustering is composed of a mean-shift step and a hierarchical clustering step. The hierarchical grouping is based upon a powerful segmentation technique previously developed. The approach is applied...
In the framework of remote-sensing image classification support vector machines (SVMs) have recently been receiving a very strong attention, thanks to their accurate results in many applications and good analytical properties. However, SVM classifiers are intrinsically noncontextual, which represents a severe limitation in image classification. In this paper, a novel method is proposed to integrate...
In case of the unknown production quality information, the clustering method with process data is used to acquire the production status. Feature extraction is an important factor to ensure the accurate rate of clustering. As a common non-linear feature extraction method, kernel principal component analysis uses the variance as the information metric. Because the variance is not always effective in...
In the last decade, there has been a growing interest in distance function learning for semi-supervised clustering settings. In addition to the earlier methods that learn Mahalanobis metrics (or equivalently, linear transformations), some nonlinear metric learning methods have also been recently introduced. However, these methods either allow limited choice of distance metrics yielding limited flexibility...
Kernel k-means algorithms have recently been shown to perform better than conventional k-means algorithms in unsupervised classification. In this paper we present is an extension of kernel k-means clustering algorithm for symbolic interval data. To evaluate this method, experiments with synthetic and real interval data sets were performed and we have been compared our method with a dynamic clustering...
The similarity measure popularly used in Kohonen's self organizing maps and several of its other variants is the mean square error (MSE). It is shown that this leads to, in information theoretic sense, a suboptimal solution of distributing the centers of the map. Here we show that using a similarity measure called the correntropy induced metric (CIM) can lead to a solution with better magnification...
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