Hierarchical addresses are fundamental to the scalability of Internet routing. The explosive of the Internet has strained the initial two-level hierarchy and led to the development of more flexible divisions between l...
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ISBN:
(纸本)0818690143
Hierarchical addresses are fundamental to the scalability of Internet routing. The explosive of the Internet has strained the initial two-level hierarchy and led to the development of more flexible divisions between levels (CIDR) and larger addresses (IPv6). Equally, important are algorithms and protocols to systematically assign addresses with appropriate hierarchical structure to allow route aggregation. This paper describes and analyzes two algorithms for clustering network nodes into a multi-level address hierarchy. We evaluate the resulting address assignment with respect to routing table size, path length and concentration of traffic. We also explicitly recognize the need for "robustness" or "slack" the assignment to accommodate future changes in topology. Our evaluation includes both single- and multi-domain topologies.
In this talk, we study some clustering algorithms with automatic selection of cluster number. Our idea is to introduce a penalty term to the objective function (i) to make the clustering process not sensitive to the i...
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ISBN:
(纸本)9781424425129
In this talk, we study some clustering algorithms with automatic selection of cluster number. Our idea is to introduce a penalty term to the objective function (i) to make the clustering process not sensitive to the initial cluster centers and (ii) to discover cluster structure in a data set. Experimental results on synthetic and real data sets are presented to demonstrate the effectiveness of the proposed algorithm. We also develop the clustering algorithm for categorical data sets and high-dimensional data sets using subspace clustering techniques. Some interesting sub-clusters and subspace clusters in data sets are discovered and reported.
This paper proposed clustering algorithms applied Gaussian basis function neural network compensator with fuzzy control for magnetic bearing system (MBS). The nonlinear MBS improved traditional bearing friction losses...
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ISBN:
(纸本)9781509024407
This paper proposed clustering algorithms applied Gaussian basis function neural network compensator with fuzzy control for magnetic bearing system (MBS). The nonlinear MBS improved traditional bearing friction losses, and nonlinear system with fuzzy controller and neural network does not require precise MBS mathematical model. We used clustering algorithms which are fuzzy c-means and k-means adjusted Gaussian basis function in neural network. Finally, we used the Lyapunov stability to guarantee MBS convergence, and the experimental results shows proposed algorithm has satisfactory performance in MBS.
According to the significant developments in information technology, there has been a massive increase in Internate of Things (IoTs) and sensors applications. Their data generation are being considered in most researc...
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ISBN:
(数字)9798350376111
ISBN:
(纸本)9798350376128
According to the significant developments in information technology, there has been a massive increase in Internate of Things (IoTs) and sensors applications. Their data generation are being considered in most researcher’s interest and focus. These applications usually produce stream and massive data. Keeping up with this massive amount of data is quite difficult. Number of mining procedures were proposed to handle specific streams of data. The clustering of data stream is among the most significant and often utilized approaches. With the single pass on the raw data sets, the infinite size of this data, and the rapid arrival of data samples, data stream clustering faces new complexities. The data’s dynamic nature, however, is the most noticeable. clustering separates the data into groups according to certain similarity. The purpose of this paper is to propose certain approach to implement clustering on certain dataset (North East Monsoon dataset in Tamil Nadu, India). Two algorithms, k-mean and “Hierarchical Density-Based Spatial clustering of Applications with Noise “(HDBSCAN), were implemented on this dataset. The HDBSCAN clustering algorithm is a flat clustering technique based on density that creates stable clusters, while K-Means is one of the most popular and trustworthy clustering algorithms. HDBSCAN algorithm performs better at gathering and locating outliers in the northeast monsoon wind data than the k-means algorithm. K-means was found executed poorly due to their initial assumptions about the clusters shape may not be met. Additionally, the idea of drift in data flows was observed and discovered using the Page-Hinkley test. It was used to explain the difference in detecting the point of change in data based on the two algorithms. Drift causes a change in the data that points to a significant climatic event.
One of the major problems in clustering is the need of specifying the optimal number of clusters in some clustering algorithms. Some block clustering algorithms suffer from the same limitation that the number of clust...
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The development of data science has brought about many discussions of noise detection, and so far, there is no universal best method. In this paper, we propose a clustering-algorithm-based solution to identify and rem...
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The development of data science has brought about many discussions of noise detection, and so far, there is no universal best method. In this paper, we propose a clustering-algorithm-based solution to identify and remove noise from air pollution data collected with mobile portable sensors. The test dataset is the air pollution data collected by the portable sensors throughout three seasons at the campus in Macao. We have applied and compared six clustering algorithms to identify the most appropriate clustering algorithm to achieve this goal: Simple K-means, Hierarchical clustering, Cascading K-means, X-means, Expectation Maximization, and Self-Organizing Map. The performance is evaluated by their accuracy and the best number of clusters calculated by the Silhouette Coefficient. Additionally, a classification algorithm J48 tree can extract the key attributes and identify the noise cluster for future unlabeled data that may contain noise. The experiment results indicate that the Expectation Maximization and Cascading Simple K-Means perform the best. Moreover, temperature and carbon dioxide are vital attributes in identifying the noise cluster.
In this paper, a comparative analysis of the mixed-type variable fuzzy c-means (MVFCM) and the fuzzy c-means using dissimilarity functions (FCMD) algorithms is presented. Our analysis is focused in the dissimilarity f...
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Reflection seismic data interpretation is an applied method in oil and gas exploration industry. Interpretation of seismic facies could help understanding complexity of internal stratigraphic geometries of complex seq...
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For many clustering algorithms, it is very important to determine an appropriate number of clusters, which is called cluster validity problem. In this paper, we offer a new approach to tackle this issue. The main poin...
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With the advent of the era of artificial intelligence and information technology, a large number of data and information pour into all walks of life. These data packages include many online and offline data such as te...
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