Dissimilarity measure plays a very important role in traditional data clustering. In this paper, we extend the dissimilarity measure as compatible measure and present a new algorithm (CNclustering) based on this measu...
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Dissimilarity measure plays a very important role in traditional data clustering. In this paper, we extend the dissimilarity measure as compatible measure and present a new algorithm (CNclustering) based on this measure. The algorithm is a rigorous partition method, it first gets some compatible clusters with a Compclustering method as the initial nucleoids, then absorbs other objects by the absorbing step to form the final clusters. We use S20 and S200 data sets to demonstrate the clustering performance of the algorithm and get some consistent results.
This thesis aims to contribute to the advancement of research in the field of ultrasound image analysis by developing several novel algorithms in three key areas: (i) modelling, analysis and validation of synthetic ul...
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This thesis aims to contribute to the advancement of research in the field of ultrasound image analysis by developing several novel algorithms in three key areas: (i) modelling, analysis and validation of synthetic ultrasound images, (ii) characterization and reduction of speckle artifacts, and (iii) enhancement of image features. These three areas have been identified based on existing gaps in research and their importance in advanced ultrasound image analysis frameworks for segmentation and classification. Synthetic models of ultrasound image formation that can generate noise-free ground truth data with intensity and texture characteristics of real ultrasound images, are valuable for machine learning applications and performance evaluation of speckle reduction techniques. This thesis develops a complete framework for synthetic ultrasound image generation incorporating algorithms for image acquisition, sampling and speckle simulation. The framework allows us to simulate image acquisition in both sector and linear scans with varying axial and lateral resolutions. Speckle artifacts appear in the form of granular noise in ultrasound images, degrading their diagnostic quality. This thesis presents novel algorithms for speckle reduction while preserving edges, fine details, and contrast of the image. The first despeckling framework presented in the thesis uses a novel application of clustering algorithms based on a transformation to wavelet sub-bands, and is inspired by the success of such methods for synthetic aperture radar imagery. The second despeckling framework uses a modified adaptive Wiener filter along with the Canny edge detection and an enhanced steerable pyramid transformation algorithm. Additionally, a coherence component extraction method is used to enhance the overall texture and edge features even in the darker portions of the image. The filtering operations used in a majority of speckle reduction methods induce blurring that affects edges and other fine
The major goal is to group users together, including in student organizations. Assume that the notion is required to cluster consumers in educational institutions. A cluster of websites or web pages organized by user ...
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In wind farm projects, the investment cost of the collection system is high, and optimizing the topological structure of the collection system can save a significant amount of cost. This paper addresses the optimizati...
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As global automation accelerates, the importance of the PCB as a core component of electronic products grows with each passing day. The smallest hazards in PCBs can cause huge losses, so testing the quality of PCBs is...
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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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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.
Ad hoc network has dynamic topology and large-scale nodes,for the purpose of monitoring and measurement in Ad hoc network,this paper proposes a new frame method of hierarchy model for monitoring,i.e.,clusterhead selec...
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Ad hoc network has dynamic topology and large-scale nodes,for the purpose of monitoring and measurement in Ad hoc network,this paper proposes a new frame method of hierarchy model for monitoring,i.e.,clusterhead selection *** mechanism designed a weight formula to handle the comprehensive update considering several factors such as distance,mobile rate,energy during the model establishment period,and three kinds of partial update means in real-time attributes *** enhances load balance and structure stability in monitor hierarchy model and reduces calculation cost for unnecessary *** simulation results show that the model using clusterhead selection mechanism was stable and efficient,which could apply to muti-scale networks,achieve conveniently and flexible deployment in Ad hoc network measurement system.
Data clustering partitions a dataset into clusters where each cluster contains similar data. clustering algorithms usually require users to set the number of clusters, e.g., k-means or fuzzy c-means. However, it is di...
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ISBN:
(纸本)9781467311830
Data clustering partitions a dataset into clusters where each cluster contains similar data. clustering algorithms usually require users to set the number of clusters, e.g., k-means or fuzzy c-means. However, it is difficult to determine a meaningful number of clusters if users lack prior knowledge of the data. Data clustering may use a validity index to grade the clustering quality. Most validity indices are based on clustering compactness and separation, but other criteria are also used for clustering. Therefore, no individual validity index is applicable to data with different properties. This paper presents a novel dynamic clustering based on particle swarm optimization. The proposed algorithm is compared with other dynamic clustering algorithms based on particle swarm optimization using artificial and real data sets. The experimental results showed that our proposed algorithm not only determines the appropriate number of clusters with correct cluster centers but can also be applied to data with different properties using various validity indices.
clustering gene sequences into families is important for understanding and predicting gene functionMany clustering algorithms and alignment-free similarity measures have been used to analyze gene familyThe clustering ...
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clustering gene sequences into families is important for understanding and predicting gene functionMany clustering algorithms and alignment-free similarity measures have been used to analyze gene familyThe clustering results can be influenced by the similarity measure and clustering algorithm usedWe compare the results from running four commonly used clustering methods, including K-means, single-linkage clustering, completelinkage clustering and average-linkage clustering, on three alignment-free similarity measuresWe try to find out which method should provide the best clustering result based on real-world gene family datasetsExperiment results show that average-linkage clustering with our similarity measure, DMk, performed best.
The Internet plays an important role in people's lives nowadays. However, Internet security is a major concern. Among the various threats facing the Internet and Internet users are so-called botnet attacks. A typi...
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ISBN:
(纸本)9781538622902
The Internet plays an important role in people's lives nowadays. However, Internet security is a major concern. Among the various threats facing the Internet and Internet users are so-called botnet attacks. A typical botnet is composed of a botmaster, a Command and Control (C&C) server and many compromised devices called bots. A botmaster can control these bots via the C&C server to launch various attacks, such as DDOS attacks, phishing, spam distribution, and so on. Among all botnets, Domain Generation algorithm (DGA) botnets are particularly resilient to traditional detection by associating the C&C server to one of the generated domains in each bot. Accordingly, this study presents a robust approach for detecting DGA botnets based on an inspection of the DNS traffic in a system. In the proposed approach, the DNS records are filtered to remove known benign or malicious domains and are then clustered using a modified Chinese Whispers algorithm. The nature of each group (i.e., malicious or benign) is then identified by means of a Sequence Similarity Module and a Query Sequence Similarity Module. It is shown that the proposed method successfully detects various types of botnet in a real-world, large scale network.
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