基于粒计算的增量式知识获取方法

张清华; 幸禹可; 周玉兰

doi:10.3724/SP.J.1146.2010.00217

基于粒计算的增量式知识获取方法

doi: 10.3724/SP.J.1146.2010.00217

张清华^{*①② 幸禹可,},
幸禹可,
周玉兰

基金项目:

重庆邮电大学博士启动基金(A2010-06)和国家自然科学基金(60573068)资助课题

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- 被引次数: 0
出版历程
- 收稿日期: 2010-03-11
- 修回日期: 2010-09-17
- 刊出日期: 2011-02-19

The Incremental Knowledge Acquisition Algorithm Based on Granular Computing

Zhang Qing-Hua^{*①② 幸禹可
,},
Xing Yu-Ke,
Zhou Yu-Lan

摘要

摘要: 该文在研究粒计算理论的基础上，提出了一种基于粒计算的增量式知识获取方法。该方法通过建立决策信息系统原始的知识粒树，对新增数据，在原始知识粒树中查找相匹配的知识粒，并依据决策值更新知识粒树，实现快速高效地处理动态信息系统。算法分析及实验对比结果表明，该方法在动态信息系统知识获取方面优于RGAGC和ID4方法。
- 数据挖掘 /
- 知识粒树 /
- 粒计算 /
- 动态信息系统 /
- 增量式知识获取
Abstract: A new incremental knowledge acquisition method based on granular computing theory is proposed. First, an original knowledge granule tree is established according to the decision-making information system. Then, for any new additional data, its matched knowledge granule in original knowledge granule tree is found at first, and then the original knowledge granule tree is updated according to the corresponding decision-making value. The new method is an efficient tool for processing dynamic data information. Both algorithm analysis and experiment results show that the new method for processing dynamic information systems and acquiring corresponding rules is superior to RGAGC and ID4 respectively.
- Data mining /
- Knowledge granule tree /
- Granular computing /
- Dynamic information systems /
- Incremental knowledge acquisition

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参考文献(1)

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