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Monitoring the evolution phases of real-time event including occurrence, development, climax, decline and ending is crucial for management department to intuitively and comprehensively understand the event and then make better decisions. However, there have been very few studies on performing phase evolution analysis of event using the number of posts at the specific time unit. The challenge of this...
Research efforts have been devoted to extraction and visualization of vortices in an unsteady (turbulent) flow. Characterizing the behaviors of the flow, vortices are identifiable as regions using a vortex detector known as the lambda2-criterion. Isosurface visualization renders vortex regions based on a chosen isovalue. However, it is highly challenging to choose one isovalue suitable for visualizing...
Recent research efforts show the benefits of using machine learning and interactive visualizations in data analytics. However, there is a void in the implementation of these techniques for the analysis of large and complex 4-dimentional (4D) unsteady flows. Hence, this paper presents an initial development of a virtual environment (VE) to fill this void. The VE has a two-layer architecture with different...
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 the paper, we propose a rigid motion segmentation algorithm with the grid-based optical flow. The algorithm selects several adjacent points among grid-based optical flows to estimate motion hypothesis based on a so-called entropy and generates motion hypotheses between two images, thus separates objects which move independently of each other. The grid-based entropy is accumulated as a new motion...
Social data from online social networks is expanding rapidly as the number of users and articles posted increases, making public opinion analysis a greater challenge. Real-time topic detection is a key part of public opinion analysis. The complex data processing involved in traditional clustering and text categorization can lead to time delays in topic detection. In this paper we construct similar...
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...
How to reduce the computation time and how to improve the quality of the clustering result are the two major research issues. Although several efficient and effective clustering algorithms have been presented, none of which is perfect. As such, an effective clustering algorithm, which is based on the prediction of searching information to determine the search directions at later iterations and employs...
One of the most well-known clustering methods for wireless sensor network is, no doubt, the so-called low energy adaptive clustering hierarchy (LEACH) because it is simple and easy to implement. Although LEACH tries to provide a fair selection mechanism by randomly selecting a number of sensors as the cluster-heads, it does not take into account the distribution of sensors, the main reason that LEACH...
Modularity is an evaluation measure for graph clustering. Louvain method is constructed by local optimization for modularity and is bottom up method as well as agglomerative hierarchical clustering. Cluster validity measures are used to evaluate cluster partitions as well as modularity. They are traditional evaluation measures in the field of clustering. We propose a novel graph clustering which is...
Detecting the groundwater runoff connectivity is important for mining and environment protection. However, traditional physical and chemical experiments based approaches are neither efficient nor effective. Experimental results have shown the bacterial community in an isolated well contains unique DNA sequences, and the bacterial communities in connected wells have common DNA sequences that are not...
Ant colony optimization (ACO) is a quite mature optimization algorithm for combinational problems, but it still attracts many researchers trying to raise its efficiency and/or performance. Some of them endeavor to speed up or improve ACO by choosing more suitable parameters of iteration or update formulas. This work tries to introduce Λ-means clustering to enhance the efficiency of ACO for the traveling...
Glaucoma is an eye disease that causes irreversible vision loss. Retinography is done manually by the ophthalmologist and is the cheapest, least invasive and most effective way to diagnose glaucoma. The ratio between the diameter of the outer part of the Optic Disc (OD) and the cup (internal part) called CDR (cup-to-disc ratio) is an important indicator of glaucoma presence in patients. This paper...
Existing clustering techniques primarily rely on prior knowledge about the data, such as the number of clusters and radii. However, in real applications, the number of clusters and the radii of clusters are usually unknown. Therefore, the performance of clustering methods with overlapping data is degraded due to their limitations in finding all cluster centers with uneven density values. Hence, a...
Clustering ensemble approaches usually have more accurate, robust and stable results than traditional single clustering approaches. However, clustering ensemble can still be improved in the following aspects: (1) improve the diversity of subspaces; (2) employ probabilistic latent clustering; (3) adopt the internal latent factor analysis before the consensus function. Therefore, we propose a new clustering...
We analyzed the search characteristics of Firefly Algorithm (FA), which has a fundamental nature of a Superior Solution Set Search Problem, previously defined in our previous study for single-objective optimization problems. In this study, we proposed a new FA method based on the former problem. This method, which employs cluster information by K-means clustering, is tested for performance by fundamental...
Clustering is a popular method to deal with the problem for mode identification of multimode processes. Unlike traditional distance-based clustering methods, in this paper, a new correlation-based bi-partition hierarchical clustering (CBHC) method is proposed, which classifies the observations according to their correlation relationships rather than their distances. Motivated by an existing correlation-based...
Spectral clustering is one of the most effective methods of data mining, in which the adjacency matrix is constructed by using the similarity matrix. In this paper, to extend spectral clustering method for uncertain data clustering, we propose a new spectral clustering method based on JS-divergence. In the proposed method, the JS-divergence is used to construct the adjacency matrix in the spectral...
Alignment is an important preprocessing step for image information retrieval. With template information, images should be aligned precisely to retrieve information inside. These images may contain repeated characters or radicals, and their textures may be not appropriate for keypoint extractions. In this paper, a novel robust image alignment algorithm is proposed for images with text template using...
Recently, some researchers attempt to find a relationship between the evolution of rare events and temporal-spatial patterns of social media activities. Their studies verify that the relationship exists in both time and spatial domains. However, few of them can accurately deduce a time point when social media activities are highly affected by a rare event. Thus, it is difficult to characterize an...
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