Considering the existing massive volumes of data processed nowadays and the distributed nature of many organizations, there is no doubt how vital the need is for distributed database systems. In such systems, the resp...
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Considering the existing massive volumes of data processed nowadays and the distributed nature of many organizations, there is no doubt how vital the need is for distributed database systems. In such systems, the response time to a transaction or a query is highly affected by the distribution design of the database system, particularly its methods for fragmentation, replication, and allocation data. According to the relevant literature, from the two approaches to fragmentation, namely horizontal and vertical fragmentation, the latter requires the use of heuristic methods due to it being NP-Hard. Currently, there are a number of different methods of providing vertical fragmentation, which normally introduce a relatively high computational complexity or do not yield optimal results, particularly for large-scale problems. In this paper, because of their distributed and scalable nature, we apply swarm intelligence algorithms to present an algorithm for finding a solution to vertical fragmentation problem, which is optimal in most cases. In our proposed algorithm, the relations are tried to be fragmented in such a way so as not only to make transaction processing at each site as much localized as possible, but also to reduce the costs of operations. Moreover, we report on the experimental results of comparing our algorithm with several other similar algorithms to show that ours outperforms the other algorithms and is able to generate a better solution in terms of the optimality of results and computational complexity.
clustering problem is an unsupervised learning problem. It is a procedure that partition data objects into matching clusters. The data objects in the same cluster are quite similar to each other and dissimilar in the ...
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clustering problem is an unsupervised learning problem. It is a procedure that partition data objects into matching clusters. The data objects in the same cluster are quite similar to each other and dissimilar in the other clusters. Density-based clusteringalgorithms find clusters based on density of data points in a region. DBSCAN algorithm is one of the density-based clusteringalgorithms. It can discover clusters with arbitrary shapes and only requires two input parameters. DBSCAN has been proved to be very effective for analyzing large and complex spatial databases. However, DBSCAN needs large volume of memory support and often has difficulties with high-dimensional data and clusters of very different densities. So, partitioning-based DBSCAN algorithm (PDBSCAN) was proposed to solve these problems. But PDBSCAN will get poor result when the density of data is non-uniform. Meanwhile, to some extent. DBSCAN and PDBSCAN are both sensitive to the initial parameters. In this paper, we propose a new hybrid algorithm based on PDBSCAN. We use modified ant clustering algorithm (ACA) and design a new partitioning algorithm based on 'point density' (PD) in data preprocessing phase. We name the new hybrid algorithm PACA-DBSCAN. The performance of PACA-DBSCAN is compared with DBSCAN and PDBSCAN on five data sets. Experimental results indicate the superiority of PACA-DBSCAN algorithm. (C) 2011 Elsevier Ltd. All rights reserved.
Development of highway transportation promotes sustainable and rapid development in economy of our country effectively. But construction of highway and transportation hub shows the nature of unbalance. So highway main...
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ISBN:
(纸本)9781424413850
Development of highway transportation promotes sustainable and rapid development in economy of our country effectively. But construction of highway and transportation hub shows the nature of unbalance. So highway main hub cities must be clustered using cluster analysis, and then divided level in order to functional analyze. K-means algorithm is the most widely rued algorithm in clustering analysis, which clustering numbers and initial clustering centers are uncertain. This paper proposes application of K-means algorithm in macroscopic planning of highway transportation hub based on ant clustering algorithm. The experimental results show this algorithm can more effectively solved clustering problem than K-means algorithm and ant clustering algorithm.
Development of highway transportation promotes sustainable and rapid development in economy of our country effectively. But construction of highway and transportation hub shows the nature of unbalance. So highway main...
详细信息
Development of highway transportation promotes sustainable and rapid development in economy of our country effectively. But construction of highway and transportation hub shows the nature of unbalance. So highway main hub cities must be clustered using cluster analysis, and then divided level in order to functional analyze. K-means algorithm is the most widely used algorithm in clustering analysis, which clustering numbers and initial clustering centers are uncertain. This paper proposes application of Kmeans algorithm in macroscopic planning of highway transportation hub based on ant clustering algorithm. The experimental results show this algorithm can more effectively solved clustering problem than Kmeans algorithm and ant clustering algorithm.
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