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论文:2014,Vol:32,Issue(2):328-333 |
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引用本文： |
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张蕴, 李伟华, 于会. BSC资源分配策略在智能决策支持系统中的应用[J]. 西北工业大学 |
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Zhang Yun, Li Weihua, Yu Hui. Applying Balanced Score Card Based Resource Allocation Strategy to Intelligence Decision Support System[J]. Northwestern polytechnical university |
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| BSC资源分配策略在智能决策支持系统中的应用 |
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| 张蕴1,2, 李伟华1, 于会1 |
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1. 西北工业大学 计算机学院, 陕西 西安 710072;
2. 西安科技大学 计算机科学与技术学院, 陕西 西安 710054 |
| 摘要: |
| 设计了一种基于平衡计分卡的数据挖掘模型,将有限资源进行合理分配,运用到智能决策支持系统的数据挖掘与智能预测当中。首先设计了基于BSC的数据挖掘总体框架,提出了3个不同的资源分配侧重方向:强调业务过程的理解,强调数据的准确度以及强调数据挖掘建模的质量;并设计了BSC资源分配模型、算法以及学习引擎,最后以实例验证了经过资源分配后的挖掘能够有效提高智能预测的准确度。基于BSC的数据挖掘不仅提供了一个用来支持不同挖掘组件的集成平台,还能够通过资源的价值分析以及资源的合理分配,将有限的资源投入到合理的挖掘过程中,以提高智能预测的准确度。 |
| 关键词:
智能决策支持系统
数据挖掘
平衡计分卡
预测
资源分配
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| Applying Balanced Score Card Based Resource Allocation Strategy to Intelligence Decision Support System |
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| Zhang Yun1,2, Li Weihua1, Yu Hui1 |
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1. Department of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an 710072, China;
2. Department of Computer Science and Engineering, Xi'an University of Science and Technology, Xi'an 710054, China |
| Abstract: |
| Sections 1 through 5 of the full paper explain our application. Section 1 is entitled "concept of BSC (balanced scorecard) based resource allocation strategy and its theoretical basis"; it explains that the BSC based strategy provides an integrated platform for supporting different mining components. Section 2 is entitled"designing BSC data mining model architecture";the architecture is illustrated by the block diagram shown in Fig. 2. Section 3 is entitled "design of weighting schemes for BSC resource allocation strategies";we design three weight distribution schemes for resource allocation. Section 4 is entitled "task models and algorithms of the resource allocation strate-gy";we define the model and design the algorithms, it is used for deecision support. Section 5 is entitled"learning engine of the BSC resource allocation strategies"; we design a learning engine whose overall process is shown in Fig. 3. Section 6 verifies the effectiveness of the BSC based strategy;the verification results, given in Fig. 6, and their analysis show preliminarily that our application of the BSC based strategy can indeed predict precisely the real data, thus improving the accuracy of data mining prediction. |
| Key words:
algorithms
artificial intelligence
computer architecture
data mining
decision support systems
design
flowcharting
forecasting
learning algorithms
mathematical models
optimization
resource allocation
balanced scorecard(BSC)
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| 收稿日期: 2013-09-23
修回日期:
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| DOI: |
| 通讯作者:
Email： |
| 作者简介: 张蕴(1983-),女,西北工业大学博士研究生,主要从事智能决策支持系统研究。
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| 相关功能 |
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| 作者相关文章 |
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| 张蕴 在本刊中的所有文章 |
| 李伟华 在本刊中的所有文章 |
| 于会 在本刊中的所有文章 |
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