Large scale terrain visualization with high-resolution has an increasing demand in many research fields. To realize the efficient rendering of terrain, this paper presents an out-of-core terrain visualization method b...
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A novel hybrid algorithm based on the AFTER (Aggregated forecast through exponential re-weighting) and the modified particle swarm optimization (PSO) is proposed. The combining weights in the hybrid algorithm are trai...
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Link analysis is an important way to discover potential web communities. This paper analyzes the characteristics of the Web link structure, and studies the traditional maximum flow algorithm and the maximum flow algor...
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Text clustering is a critical step in text data analysis and has been extensively studied by the text mining community. Most existing text clustering algorithms are based on the bag-of-words model, which faces the hig...
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Maintaining Generalized Arc Consistency (GAC) during search is considered an efficient way to solve non-binary constraint satisfaction problems. Bit-based representations have been used effectively in Arc Consistency ...
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Maintaining Generalized Arc Consistency (GAC) during search is considered an efficient way to solve non-binary constraint satisfaction problems. Bit-based representations have been used effectively in Arc Consistency algorithms. We propose STRbit, a GAC algorithm, based on simple tabular reduction (STR) using an efficient bit vector support data structure. STRbit is extended to deal with compression of the underlying constraint with c-tuples. Experimental evaluation show our algorithms are faster than many algorithms (STR2, STR2-C, STR3, STR3-C and MDDc) across a variety of benchmarks except for problems with small tables where complex data structures do not payoff.
In order to distinguish and extract the topic information from other interferential information on the BBC news website for the study in social computing, the BBC News Hunter was proposed in this paper. The whole syst...
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To obtain the optimal partition of a data set, a hybrid clustering algorithm, PKPSO, based on PSO is proposed. In the proposed PKPSO the PSO algorithm is effectively integrated with the K means algorithm. Among the po...
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To obtain the optimal partition of a data set, a hybrid clustering algorithm, PKPSO, based on PSO is proposed. In the proposed PKPSO the PSO algorithm is effectively integrated with the K means algorithm. Among the population, selected candidate solutions are further optimized to improve the accuracy by the K-means algorithm. By analyzing the algorithm, the criterions for control parameters selection are determined. Partional clustering result by the proposed PKPSO is compared with that by PSO or by K-means algorithm, and results show that the global convergent property of PKPSO is better than that of the other algorithms. The PKPSO can not only overcome the shortcoming of local minimum trapping of the K-means, but also the solution precision and algorithm stability are better than that of the other two algorithm.
To solve the problem of searching for an optimal elimination ordering of Bayesian networks, a novel effective heuristic, MinSumWeight, and an ACS approach incorporated with multi-heuristic mechanism are proposed. The ...
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A Spectrum-based Support Vector Algorithm (SSVA) to resolve semi-supervised classification for relational data is presented in this paper. SSVA extracts data representatives and groups them with spectral analysis. Lab...
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A novel self Adaptive Support Vector Clustering algorithm (ASVC) is proposed in this paper to cluster dataset with diverse dispersions. And a Kernel function is defined to measure affinity between multi-relational dat...
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