Considering the problem that intrusion detection systems always produced duplicated alarm information, in this paper we propose an iterative self-organization clustering algorithm. It begins with calculating average v...
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Considering the problem that intrusion detection systems always produced duplicated alarm information, in this paper we propose an iterative self-organization clustering algorithm. It begins with calculating average value of classes as the new clustering center on the basis of random selection, merging and dividing dynamically, then finish the clustering procedure through the iteration finally. Experimental results with DARPA1999 testing data set show that the clustering method is more excellent than traditional clustering methods in both aggregation rate and error aggregation rate. Besides, it reduces duplicated alarm effectively and provides assistance to further related work.
An adaptive parallel algorithm for hierarchical clustering based on PRAM model was presented. The following approaches were devised to produce the optimized clustered data set, including the data preprocessing based o...
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An adaptive parallel algorithm for hierarchical clustering based on PRAM model was presented. The following approaches were devised to produce the optimized clustered data set, including the data preprocessing based on "90-10" rule to decrease the size of the data set, progressively the parallel algorithm to create Euclid minimum spanning trees on absolute graph, and the algorithm that determined the split strategies and dealt with the memory conflicts. The data set was clustered based on the noncollision memory, the lowest cost, and weakest PRAM-EREW model. N data sets were clustered in O((lambda n)(2)/p) time (0.1 <= lambda <= 0.3) by performing this algorithm using p processors (1 <= p <= n/log(n)). The parallel hierarchical clustering algorithm based on PRAM model was adaptive, and of noncollision memory. The computing time could be significantly reduced after original inputting data was effectually preprocessed through the improved preprocessing methods presented in this paper.
Topology construction, the initial step of the topology control, is an important technique for a wireless ad hoc network to be energy-efficient. In [4], we have proposed to use the relative neighborhood graph (RNG) to...
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ISBN:
(纸本)9781457720536
Topology construction, the initial step of the topology control, is an important technique for a wireless ad hoc network to be energy-efficient. In [4], we have proposed to use the relative neighborhood graph (RNG) to obtain an RNG-based topology in which the transmission ranges between wireless nodes are reduced. Then, among the RNG-based topology, we proposed a green clustering algorithm (GCA) to organize the wireless nodes into a clustered network topology. In this paper, we further analyze the energy consumption in exchanging data packets and cluster maintenance messages. Simulation results confirm that the proposed RGCA (i.e., combining the RNG and GCA) provides a way to construct an energy-efficient cluster topology for wireless ad hoc networks.
A 3D feature space is proposed to represent visual complexity of images based on Structure, Noise, and Diversity (SND) features that are extracted from the images. By representing images using the proposed feature spa...
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A 3D feature space is proposed to represent visual complexity of images based on Structure, Noise, and Diversity (SND) features that are extracted from the images. By representing images using the proposed feature space, the human classification of visual complexity of images as being simple, medium, or complex can be implied from the structure of the space. The structure of the SND space as determined by a clustering algorithm and a fuzzy inference system are then used to assign visual complexity labels and values to the images respectively. Experiments on Corel 1000A dataset, Web-crawled, and Caltech 256 object category dataset with 1000, 9907, and 30607 images respectively using MATLAB demonstrate the capability of the 3D feature space to effectively represent the visual complexity. The proposal provides a richer understanding about the visual complexity of images which has applications in evaluations to determine the capacity and feasibility of the images to tolerate image processing tasks such as watermarking and compression.
Fuzzy frequency response estimation: a case study for the pH neutralization process is proposed in this paper. A fuzzy c-Means clustering algorithm is used to organize the output experimental data of pH neutralization...
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ISBN:
(纸本)9781467315074
Fuzzy frequency response estimation: a case study for the pH neutralization process is proposed in this paper. A fuzzy c-Means clustering algorithm is used to organize the output experimental data of pH neutralization process into groups based on similarities among the data. Starting from the fuzzy clustering a methodology for identification of the linear sub-models, in terms of transfer function, that represent the pH neutralization process in operation regions is developed and these linear sub-models are organized according to Takagi-Sugeno (TS) fuzzy representation. A fuzzy frequency estimation of pH neutralization process is determined through the frequency response function. The main contribution of this paper is to demonstrate through the proposal of a Theorem, that fuzzy frequency response estimation is a region in the magnitude and phase Bode plots.
This paper proposes three novel parallel clustering algorithms based on the Kohonen's SOM aiming at preserving the topology of the original dataset for a meaningful visualization of the results and for discovering...
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This paper proposes three novel parallel clustering algorithms based on the Kohonen's SOM aiming at preserving the topology of the original dataset for a meaningful visualization of the results and for discovering associations between features of the dataset by topological operations over the clusters. In all these algorithms the data to be clustered are subdivided among the nodes of a GRID. In the first two algorithms each node executes an on-line SOM, whereas in the third algorithm the nodes execute a quasi-batch SOM called MANTRA. The algorithms differ on how the weights computed by the slave nodes are recombined by a master to launch the next epoch of the SOM in the nodes. A proof outline demonstrates the convergence of the proposed parallel SOMs and provides indications on how to select the learning rate to outperform both the sequential SOM and the parallel SOMs available in the literature. A case study dealing with bioinformatics is presented to illustrate that by our parallel SOM we may obtain meaningful clusters in massive data mining applications at a fraction of the time needed by the sequential SOM, and that the obtained classification supports a fruitful knowledge extraction from massive datasets. (C) 2011 Elsevier B.V. All rights reserved.
This paper proposes a robust Criticality-based clustering algorithm (CCA) for Vehicular Ad Hoc NETworks (VANETs) based on the concept of network criticality. Network criticality is a global metric on an undirected gra...
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ISBN:
(纸本)9781457720529
This paper proposes a robust Criticality-based clustering algorithm (CCA) for Vehicular Ad Hoc NETworks (VANETs) based on the concept of network criticality. Network criticality is a global metric on an undirected graph, that quantifies the robustness of the graph against environmental changes such as topology. In this paper, we localize the notion of network criticality and apply it to control cluster formation in the vehicular wireless network. We use the localized notion of node criticality together with a universal link measure, Link Expiration Time (LET), to derive a distributed multi-hop clustering algorithm for VANETs. Simulation results show that the proposed CCA forms robust cluster structures.
A semi-fuzzy c-means algorithm based on revised Euclidean distance was proposed to improve the real-time capability and precision in fault pattern classified. Effectiveness of threshold parameters on clustering was in...
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ISBN:
(纸本)9781457714146
A semi-fuzzy c-means algorithm based on revised Euclidean distance was proposed to improve the real-time capability and precision in fault pattern classified. Effectiveness of threshold parameters on clustering was investigated, and then program steps were given. The example of fault diagnosis in an airborne fire control system BIT was developed. The results show that the new algorithm can recognize fault pattern adaptively and precisely.
In this work, we propose DEformable BAyesian Networks (DEBAN), a probabilistic graphical model framework where model selection and statistical inference can be viewed as two key ingredients in the same iterative proce...
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ISBN:
(纸本)9780819485915
In this work, we propose DEformable BAyesian Networks (DEBAN), a probabilistic graphical model framework where model selection and statistical inference can be viewed as two key ingredients in the same iterative process. While this concept has shown successful results in computer vision community, 1-4 our proposed approach generalizes the concept such that it is applicable to any data type. Our goal is to infer the optimal structure/model to fit the given observations. The optimal structure conveys an automatic way to find not only the number of clusters in the data set, but also the multiscale graph structure illustrating the dependence relationship among the variables in the network. Finally, the marginal posterior distribution at each root node is regarded as the fused information of its corresponding observations, and the most probable state can be found from the maximum a posteriori (MAP) solution with the uncertainty of the estimate in the form of a probability distribution which is desired for a variety of applications.
This paper proposes an online clustering algorithm for wind speed forecasting. The algorithm combines the persistence method and the RBF neural network, and chooses an appropriate method according to different wind co...
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ISBN:
(纸本)9781424487363
This paper proposes an online clustering algorithm for wind speed forecasting. The algorithm combines the persistence method and the RBF neural network, and chooses an appropriate method according to different wind conditions. Computer simulations demonstrate that this algorithm can more accurately predict wind speed than either of the single methods and therefore is more effective for wind speed forecasting.
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