The offline components of CluStream clustering algorithm based on distance, and it is difficult to find non-spherical character of the cluster. This paper proposes a data streams clustering algorithm based on grid and...
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ISBN:
(纸本)9780769539300
The offline components of CluStream clustering algorithm based on distance, and it is difficult to find non-spherical character of the cluster. This paper proposes a data streams clustering algorithm based on grid and particle swarm optimization, the algorithm based on two-tier structure of CluStream clustering algorithm. The grid feature vector to represent a snapshot, the grid density, and grid merging technologies is applied in this algorithm. So we can found any non-spherical clusters,In this ***-dense grid is periodic and dynamic way to remove,which is good for reducing the space complexity. Using the PSO optimized clustering results in the offline components, in order to get a more precise clustering efficiency. Experiments show that this algorithm is more efficient than the CluStream algorithms, it has a good number of dimensions scalability,and it can find non-spherical nature of the clustering results.
Gray-level clustering is an important procedure in image processing, which reduces the gray-level of an image. In order to display an image with high gray level in a screen with lower gray level, a good gray-level clu...
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ISBN:
(纸本)9780769535197
Gray-level clustering is an important procedure in image processing, which reduces the gray-level of an image. In order to display an image with high gray level in a screen with lower gray level, a good gray-level clustering algorithm is necessary to complete this job. Based on the mean value and standard deviation of histogram within a sub-interval, a novel recursive algorithm for solving the gray-level reduction is proposed in this paper. It divides the sub-interval recursively until the difference between original image and clustered image within a given threshold. Experiments are carried out for some samples with high gray level to demonstrate the computational advantage of the proposed method.
The paper presents a meta-search tool developed by the authors in order to deliver search results structured according to the specific interests of the users. Meta-search means that for a specific query, several searc...
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ISBN:
(纸本)9780982148914
The paper presents a meta-search tool developed by the authors in order to deliver search results structured according to the specific interests of the users. Meta-search means that for a specific query, several search mechanisms could be applied. Using the clustering process, thematically homogenous groups are building up from the initial list provided by the standard search mechanisms. To identify the clusters, the characteristics of the initial results are used, without any predefined categories.
In wireless sensor networks, the data-centric sensing task focuses more on the data reliability. In this paper, a reliable clustering algorithm RCVC (Reliable clustering using Virtual Cluster-head) is proposed. It pro...
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ISBN:
(纸本)9781424427239
In wireless sensor networks, the data-centric sensing task focuses more on the data reliability. In this paper, a reliable clustering algorithm RCVC (Reliable clustering using Virtual Cluster-head) is proposed. It provides multipath for nodes in the network by using a set of redundant cluster-heads which are marked with a unique virtual identification. When the active cluster-heads fail, according to a certain strategy, our algorithm repairs the communication failures by selecting a backup node front the set of redundant cluster-heads. Simulation results show that RCVC can effectively reduce the influence of cluster-head failure on the performance of algorithm and improve the reliability of communication in the network.
With the development of computer technology, network security has become an important issue of concern. In view of the growing number of network security threats and the current intrusion detection system development,...
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ISBN:
(纸本)9780769539300
With the development of computer technology, network security has become an important issue of concern. In view of the growing number of network security threats and the current intrusion detection system development, this paper gives a new model of anomaly intrusion detection based on clustering algorithm. Because of the k-means algorithm's shortcomings about dependence and complexity, the paper puts forward an improved clustering algorithm through studying on th traditional means clustering algorithm. The new algorithm learns the strong points from the k-medoids and improved relations trilateral triangle theorem. The experiments proved that the new algorithm could improve accuracy of data classification and detection efficiency significantly. The results show that this algorithm achieves the desired objectives with a high detection rate and high efficiency.
We have recently suggested using clustering of variables (CLoVA) based on unsupervised pattern recognition for partitioning variables into informative and redundant ones. Because data clustering plays a central role i...
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We have recently suggested using clustering of variables (CLoVA) based on unsupervised pattern recognition for partitioning variables into informative and redundant ones. Because data clustering plays a central role in CLoVA, in the present study, we compared the efficiency of different clustering methods including the Kohonen self-organizing map (SOM), principal component analysis, fuzzy c-means clustering, K-means clustering, and hierarchical cluster analysis for clustering spectroscopic data and molecular descriptors to build multivariate calibration and quantitative structure-activity (QSAR) models. To investigate which clustering methods are more efficient for CLoVA, four data sets (three spectroscopic and one QSAR) were analyzed. Most of the CLoVA-based models obtained by SOM resulted in the least root mean square errors of cross-validation and prediction, suggesting a higher efficiency of SOM for clustering variables. In all cases, the results obtained by the CLoVA-based method were compared with those obtained by conventional principal component regression as well as genetic algorithm and successive projection algorithm partial least square regression. Interestingly, models produced by the CLoVA-based method were more predictive with respect to that of the other methods, as indicated by the lowest root mean square error of prediction. Copyright (c) 2013 John Wiley & Sons, Ltd.
K-means algorithm is a popular method in clustering analysis. After reviewing the traditional K-means algorithm, we proposed an improved K-means algorithm. At first we select the Euclidean distance or Manhattan distan...
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ISBN:
(纸本)9781424441334
K-means algorithm is a popular method in clustering analysis. After reviewing the traditional K-means algorithm, we proposed an improved K-means algorithm. At first we select the Euclidean distance or Manhattan distance as distance measure in our algorithm through calculating the rule of distance measure. Different initial centroids lead to different results. So the next step we will select the initial centroids which are consistent with the distribution of data. According to simulation, the improved K-means algorithm has can achieve higher accuracy and stability than the traditional ones.
It is significantly important to study the hot topic detection and tracking technology(TDT) since the online information has become the major source to inform the mass by means of the network news and forum, which has...
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It is significantly important to study the hot topic detection and tracking technology(TDT) since the online information has become the major source to inform the mass by means of the network news and forum, which has been manifested itself in being hefty, swift dissemination, dynamic interaction. In this article, a brief review of the current application of the TDT will be made aiming at the prospect of the research of introduction along with other key technologies relevant to the area at home and abroad.
Crowdsourcing-based localization has attracted wide research concern to the metropolitan-scale positioning. However, crowdsourcing-based fingerprints collection with assorted mobile smart devices brings fingerprint co...
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ISBN:
(纸本)9781467319553;9781467319546
Crowdsourcing-based localization has attracted wide research concern to the metropolitan-scale positioning. However, crowdsourcing-based fingerprints collection with assorted mobile smart devices brings fingerprint confusion, which significantly degrades the localization accuracy. To solve the device diversity problem, many solutions have been raised like the Device-clustering algorithm. Based on macro Device-Cluster (DC) rather than natural device, DC algorithm maintains less device types and slight calibration overhead. Despite high positioning accuracy, the selection of suitable clustering algorithms in DC system becomes another puzzle. In this paper, we reshape the novel Device-clustering algorithm to enhance the indoor positioning by comparing the application of different clustering algorithms. The experimental result indicates the reliability of DC strategy in broad clustering scheme as well as the suitable locating process corresponding to distinct environment.
Hyperspectral image classification is an important part of the hyperspectral remote sensing information processing. The Iterative Selforganizing Data Analysis Techniques algorithm (ISODATA) clustering algorithm which ...
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ISBN:
(纸本)9781479958368
Hyperspectral image classification is an important part of the hyperspectral remote sensing information processing. The Iterative Selforganizing Data Analysis Techniques algorithm (ISODATA) clustering algorithm which is an unsupervised classification algorithm is considered as an effective measure in the area of processing hyperspectral images. In this paper, an improved ISODATA algorithm is proposed for hyperspectral images classification. The algorithm takes the maximum and minimum spectrum of the image into consideration and determines the initial cluster center by the stepped construction of spectrum accurately. The classification experiment results show that using the improved ISODATA algorithm can determine the initial cluster number adaptively. In comparison with the SAM (Spectral Angle Mapper) algorithm and the original ISODATA algorithm, a better performance of the proposed ISODATA method is shown in the part of results.
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