In this paper, we analyze some clustering algorithms that have been widely employed in the past to support the comprehension of web applications. To this end, we have defined an approach to identify static pages that ...
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ISBN:
(纸本)9781424414505
In this paper, we analyze some clustering algorithms that have been widely employed in the past to support the comprehension of web applications. To this end, we have defined an approach to identify static pages that are duplicated or cloned at the content level. This approach is based on a process that first computes the dissimilarity between web pages using Latent Semantic Indexing, a well known information retrieval technique, and then groups similar-pages using clustering algorithms. We consider five instances of this process, each based on three variants of the agglomerative hierarchical clustering algorithm, a divisive clustering algorithm, k-means partitional clustering algorithm, and a widely employed partitional competitive clustering algorithm, namely Winner Takes All. In order to assess the proposed approach, we have used the static pages of three web, applications and one static web site.
Transmissions in the mmWave spectrum benefit from a-priori knowledge of radio channel propagation models. This paper is concerned with one important task that helps provide a more accurate channel model, namely, the c...
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ISBN:
(纸本)9781538683804
Transmissions in the mmWave spectrum benefit from a-priori knowledge of radio channel propagation models. This paper is concerned with one important task that helps provide a more accurate channel model, namely, the clustering of all multipath components arriving at the receiver. Our work focuses on directive transmissions in urban outdoor scenarios and shows the importance of the correct estimation of the number of clusters for mmWave radio channels simulated with a software ray-tracer tool. We investigate the effectiveness of k-means and k-power-means clustering algorithms in predicting the number of clusters through the use of cluster validity indices (CMIs) and score fusion techniques. Our investigation shows that clustering is a difficult task because the optimal number of clusters is not always given by one or by a combination of more CMIs. However, using score fusion methods, we find the optimal partitioning for the k-means algorithm based on the power and time of arrival of the multipath rays or based on their angle of arrival. When the k-power-means algorithm is used, the power of each arriving ray is the most important clustering factor, making the dominant received paths pull the other ones around them, to form a cluster. Thus, the number of clusters is smaller and the decision based on CMIs or score fusion factors easier to be taken.
This paper presents the performance of seven portfolios created using clustering analysis techniques to sort out assets into categories and then applying classical optimization inside every cluster to select best asse...
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This paper presents the performance of seven portfolios created using clustering analysis techniques to sort out assets into categories and then applying classical optimization inside every cluster to select best assets inside each asset category. The proposed clustering algorithms are tested constructing portfolios and measuring their performances over a two month dataset of 1-minute asset returns from a sample of 175 assets of the Russell 1000 index. A three-week sliding window is used for model calibration, leaving an out of sample period of five weeks for testing. Model calibration is done weekly. Three different rebalancing periods are tested: every 1, 2 and 4 hours. The results show that all clustering algorithms produce more stable portfolios with similar volatility. In this sense, the portfolios volatilities generated by the clustering algorithms are smaller when compare to the portfolio obtained using classical Mean-Variance Optimization (MVO) over all the dataset. Hierarchical clustering algorithms achieve the best financial performance obtaining an adequate trade-off between accumulated financial returns and the risk-adjusted measure, Omega Ratio, during the out of sample testing period. (C) 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the International Conference on Computational Science
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)9781509024391
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.
The French company EDF uses supervisory control and data acquisition systems in conjunction with a data management platform to monitor hydropower plant, allowing engineers and technicians to analyse the time-series co...
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Data Mining is an analytical procedure proposed to have an insight of consistent patterns or logical associations between variables, and further the findings are validated by applying the identified patterns to genera...
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Fuzzy c-means algorithm (FCM) based on Euclidean distance function converges to a local minimum of the objective function, which can only be used to detect spherical structural clusters. In this paper, an improved Fuz...
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clustering is one of the most prevalent methods to construct multi-templates from training data. However, most of clustering algorithms proposed in the literature aim at minimizing not the recognition error, but the s...
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The self-motivated environment of mobile nodes confronts the connectivity and the stability in mobile MANETs. In MANETs, nodes are usually defined as a network which is created using multiple free or autonomous mobile...
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We consider clustering problems under two different optimization criteria. One is to minimize the maximum intra-cluster distance (diameter), and the other is to maximize the minimum intercluster distance. In particula...
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