As electronic commerce (EC) become more and more prevalent to people, it is very critical to provide the right information to the right customers. The EC sites are generating large amount of data on customer purchases...
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As electronic commerce (EC) become more and more prevalent to people, it is very critical to provide the right information to the right customers. The EC sites are generating large amount of data on customer purchases. Data mining techniques in EC domain is currently a hot research area. This paper proposes a rough set based approach to obtaining patterns of goods and classifying associated goods into groups, which can be used by the sites to group their goods according to the customers' preference and adopt appropriate selling policies.
In this paper, a novel thresholding algorithm is presented to achieve improved image segmentation performance at low computational cost. The proposed algorithm uses the normalized graph cut measure as the thresholding...
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In this paper, a novel thresholding algorithm is presented to achieve improved image segmentation performance at low computational cost. The proposed algorithm uses the normalized graph cut measure as the thresholding principle to distinguish an object from the background, as such fair treatment of different sets of diversified sizes is ensured. The weight matrices used in evaluating the graph cuts are based on the gray levels of an image, rather than the commonly used image pixels. For most images, the number of gray levels is much smaller than the number of pixels. Therefore, the proposed algorithm occupies much smaller storage space and requires much lower computational costs and implementation complexity than other graph-based image segmentation algorithms. This fact makes the proposed algorithm attractive in various real-time vision applications such as automatic target recognition (ATR). A large number of examples are presented to show the superior performance of the proposed thresholding algorithm compared to existing thresholding algorithms.
Grid scheduling which aims at improving resource utilization and grid application performance is a key concern in grid. Currently, much research can be found about grid scheduling and some algorithms on it were propos...
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Grid scheduling which aims at improving resource utilization and grid application performance is a key concern in grid. Currently, much research can be found about grid scheduling and some algorithms on it were proposed. However, since grid resources are autonomic, distributed and their status change over time, those scheduling algorithms did not fit for the cases well. In this paper, a cache based feedback grid scheduling (CBFS) approach is presented to capture the dynamics and impact of simultaneously co-allocated tasks in a grid. In this approach, grid scheduler utilizes recent resource performance data, such as recent task submitting time and execution time of task which are kept in cache and a feedback approach to engineer load balancing across multiple grid resources. After comparing this dynamic grid scheduling approach with previous research, it is found that CBFS is more generous than other scheduling approaches. Experimental results demonstrate that this approach diminishes latency and contributes to the overall grid load balancing, which significantly improves resource utilization and response time of tasks.
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