| 基于扩展粗糙集模型的近似概念格规则挖掘研究 |
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| 引用本文： | 丁卫平,;管致锦,;石振国.基于扩展粗糙集模型的近似概念格规则挖掘研究[J].南京邮电学院学报(自然科学版),2009(2):10-15. |
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| 作者姓名： | 丁卫平 ;管致锦 ;石振国 |
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| 作者单位： | [1]南通大学计算机科学与技术学院,江苏南通226019; [2]南京航空航天大学信息科学与技术学院,江苏南京210016; [3]上海大学计算机工程与科学学院,上海200072 |
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| 基金项目： | 国家自然科学基金.微软亚洲研究院联合项目（60873069）、江苏省高校自然科学基础研究项目（07KJB520096）、南通市应用研究计划（K2008031）、南通大学自然科学基金（052061）、南通大学通信与信息系统学科科技创新资助项目 |
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| 摘 要： | 粗糙集和概念格是两种不同的知识发现和数据挖掘有效工具，已被广泛应用于许多领域。在对粗糙集和概念格基本理论研究基础上，提出了利用扩展粗糙集模型对概念格近似使其得以改进，即在概念格中引入β-多数蕴涵关系实现概念格中结点近似合并以及近似概念格（ACL）的构建，由此提出概念格粗糙近似和规则挖掘算法（LCRA）。最后通过UCI机器学习数据库相关测试表明该算法的可行性和有效性。
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| 关 键 词： | 粗糙集 β-多数蕴涵关系 近似概念格 规则挖掘 |
Research of Approxima Concept Lattice and Rules Mining Based on Extended Rough Sets Model |
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| Affiliation: | DING Wei-ping, GUAN Zhi-jing,2, SHI Zhen-guo(1.School of Computer Science ＆ Technology, Nantong University, Nantong 226019, China;2.College of Information Science and Technology,Nanjing University of Aeronautics and Astronauties,Nanjing 210016 ,China 3. School of Computer Engineering and Science, Shanghai University, Shanghai 200072, China) |
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| Abstract: | Rough sets and concept lattice, two different effective tools for konwledge discovery and data mining, are successfully applied to many fields. After the correlative ideas of rough sets and concept lattice are studies, the method of concept lattice approximated and improved is put forward by extending rough sets model. An approximate concept latttice（ACL） is constructed by using the β-partial contain relation. Thereby the algorithm named LCRA of Rough approximation in concept lattice and association mining is described out. Finally the experimental results based on UCI machine learning data sets demonstrate that the proposed algorithm is the feasible and effective. |
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| Keywords: | rough sets β-partial contain relation approximate concept lattice rules mining |
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