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.
In this paper, a new algorithm for visualization of high-multidimensional data is described. The algorithm follows several steps. At first, centers representing several categories are selected, and Euclidean distances between these centers are calculated in a high-dimensional space. Then these centers are placed in a 2-dimensional space in such a way that distances in this 2-dimensional space are...
The performance of objective function-based fuzzy clustering algorithms depends on the shape and the volume of the clusters, the initialization of the clustering algorithm, the distribution of the data objects, and the number of clusters contained in the data. We propose an extension of Gustafson- Kessel (FGK) fuzzy algorithm by developing adaptive validation criteria for merging of clusters during...
Clustering algorithm can be used for exploring deep information from data base and outlining the characteristics of each class. In this paper, some typical clustering algorithms were reviewed. Then we focused on classifying G-band bright points (GBPs) in high resolution solar photospheric images. It is found that K-means is suitable for detecting the noise points and DBSCAN algorithm is good at extracting...
Data clustering is one of the popular tasks recently used in the educational data mining arena for grouping similar students by several aspects such as study performance, behavior, skill, etc. Many well-known clustering algorithms such as k-means, expectation-maximization, spectral clustering, etc. were employed in the related works for educational data clustering. None of them has taken into consideration...
It is important to distinguish overlapped cell for tracking and segmentation biological cells from images. In this research, a novel or comprehensive method is provided with morphological features of cell and minimum distance for overlapped cells separation (OCS). it’s necessary to say that this algorithm is not based on type and number of Overlapped cells. In this presented method based on a distance...
In this study we analyzed a series of LiDAR point clouds acquired over Taijiang district (part of Fujian province, China). The objective was to detect and extract water surface area from individual LiDAR point cloud, in a parallel means. To this end, interactive visualization of fine-grained data, global cluster algorithms, and statistical investigation were applied. We first rasterized point clouds...
Recently, fiber clustering algorithms have become an important tool in neuroscience for grouping the white matter tracts into anatomically meaningful bundles. The results of clustering can be used for quantification and comparison between different brains to find out abnormalities or unusual features. One essential problem in fiber clustering is to provide a similarity measure for a pair of fibers...
The unsupervised analysis of data-sets, both large in dimension as well as in number of objects, are one of the most challenging tasks in data intense sciences. Especially in astronomy, dedicated survey telescopes generate an enormous amount of complex data. For example the database of the Sloan Digital Sky Survey (SDSS DR10) contains 3 million spectra with ca. 5,000 values each. Analyzing those spectra...
Finding an object in a 3D scene is an important problem in the robotics, especially in assistive systems for visually impaired people. In most systems, the first and most important step is how to detect an object in a complex environment. In this paper, we propose a method for finding an object using geometrical constraints on depth images from a Kinect. The main advantage of the approach is it is...
Virtual Coordinate (VC) based Wireless Sensor Networks (WSNs) are susceptible to attacks resulting from malicious modification of VCs of individual nodes. While the impact of some such attacks is localized, others such as coordinate deflation and wormholes (tunneling) can cause severe disruptions. A comprehensive solution for detection of such attacks is presented that combines Beta Reputation System...
Cluster analysis is a popular technique in statistics and computer science with the objective of grouping similar observations in relatively distinct groups generally known as clusters. In this paper we propose an approach called Manifold Density Peaks Clustering to improve the basic density peaks clustering. It mainly concerns three aspects. First, geodesic distance is adopted to calculate manifold...
This paper proposes a density grid-based algorithm (C_UStream) for clustering on uncertain data stream in sliding window which can find clusters of arbitrary shapes. The statistical summary information of each grid is stored in linked queue structure by using sampling window mechanism. In order to guarantee the validity of clustering, the expired grids in the current window are removed regularly....
This paper proposed an automatic clustering algorithm based on entropy for discovering the interest pattern over users' web log. We introduced the information entropy on the basis of clustering algorithm. Compared with traditional clustering algorithms, our method does not require any parameters specified by the end user. Meanwhile, it can discover the clusters in arbitrary shape and size. Experimental...
Chromatographic signals have a specific microscopic behaviour which enables to statistically model the retention time of molecules. Such microscopic point of view is adopted in this paper for addressing the inverse problem of chromatographic profiles inference in a Nonparametric Bayesian framework in order to propose an automatic unsupervised alternative to the traditional chemometrics methods. An...
The technical advances of positioning technologies enable us to track animal movements at finer spatial and temporal scales, and further help to discover a variety of complex interactive relationships. In this paper, considering the loose gathering characteristics of the real-life groups' members during the movements, we propose two kinds of loose group movement patterns and corresponding discovery...
The natural contour extraction during non-rigid object tracking is a challenging task in computer vision. Most tracking-by-detection methods are based on rectangular bounding-boxes, and this leads to compounding tracking errors in subsequent frames. This paper present an accurate natural contour tracking method for non-rigid object in video, there are three main contributions. Firstly, we combined...
Superpixels are an oversegmentation of an image and popularly used as a preprocessing in many computer vision applications. Many state-of-the-art superpixel segmentation algorithms rely either on minimizing special energy functions or on clustering pixels in the effective distance space. While in this paper, we introduce a novel algorithm to produce superpixels based on the edge map by utilizing a...
DoS (Denial of Service) and DDoS (Distributed Denial of Service) is an anomalous traffic phenomena that is need serious attention. In the previous research has already been discussed on traffic anomaly detection based on clustering, with a hierarchical clustering algorithm method. In this paper, we introduce a method of network traffic anomaly (DDoS) detection using modernization of the traditional...
A novel fuzzy clustering algorithm is presented in this paper, which removes the constraints generally imposed to the cluster shape when a given model is adopted for membership functions. An on-line, sequential procedure is proposed where the cluster determination is performed by using suited membership functions based on geometrically unconstrained kernels and a point-to-shape distance evaluation...
Classical unmixing algorithms focus primarily on scenarios with a single mixture. These techniques are easily extensible in the case of images with multiple discrete mixtures (i.e. no shared endmembers). Unmixing in scenarios with multiple mixtures with shared or common endmembers is significantly harder. Manifold clustering and embedding seem tailor-made for such a scenario, but generally these algorithms...
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.