| A Fast Scalable Classifier Tightly Integrated with RDBMS |
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| 作者姓名： | 刘红岩 陆宏钧 陈剑 |
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| 作者单位： | [1]SchoolofEconomicsandManagement,TsinghuaUniversity,Beijing100084,P.R.China [2]DepartmentofComputerScienceHongKongUniversityofScience＆Technology,HongKong,P.R.China |
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| 基金项目： | 面向21世纪教育振兴行动计划(985计划) |
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| 摘 要： | In this pager,we report our success in building efficient scalable classifiers by exploring the capabilities of modern relational database management systems (RDBMS).In addition to high classification accuracy,the unique features of the approach include its high training speed ,linear scalability,and simplicity in implementation.More importantly,the major computation required in the approach can be implemented using standard functions provided by the modern realtional DBMS.Besides,with the effective rule pruning strategy,the algorithm proposed in this paper can produce a compact set of classification rules,The results of experiments conducted for performance evaluation an analysis are presented.
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| 关 键 词： | 关系数据库 紧密集成 快速升级分类器 |
| 收稿时间： | 05 February 2009 |
A fast scalable classifier tightly integrated with RDBMS |
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| Hongyan Liu,Hongjun Lu,Jian Chen.A Fast Scalable Classifier Tightly Integrated with RDBMS[J].Journal of Computer Science and Technology,2002,17(2):152-159. |
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| Authors: | Hongyan Liu Hongjun Lu Jian Chen |
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| Affiliation: | (1) School of Economics and Management, Tsinghua University, 100084 Beijing, P.R. China;(2) Department of Computer Science, Hong Kong University of Science & Technology, Hong Kong, P.R. China |
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| Abstract: | In this paper, we report our success in building efficient scalable classifiers by exploring the capabilities of modern relational
database management systems (RDBMS). In addition to high classification accuracy, the unique features of the approach include
its high training speed, linear scalability, and simplicity in implementation. More importantly, the major computation required
in the approach can be implemented using standard functions provided by the modern relational DBMS. Besides, with the effective
rule pruning strategy, the algorithm proposed in this paper can produce a compact set of classification rules. The results
of experiments conducted for performance evaluation and analysis are presented.
This work is supported by the Tsinghua University 985 Basic Research Project (No.091101004).
LIU Hongyan received her Ph.D. degree in management science and engineering from Tsinghua University in 2000. Now she is a lecturer in
the School of Economics and Management, Tsinghua University. Her major research interests include database, neural network,
data warehousing, and data mining.
LU Hongjun received his Ph.D. degree in computer science from the University of Wisconsin in 1985. Now he is a professor in the Computer
Science Department, Hong Kong University of Science and Technology. His major research interests include data/knowledge base
management systems, physical database design and database performance, data warehousing, and data mining.
CHEN Jian received his Ph.D. degree in system engineering from Tsinghua University in 1989. Now he is a full professor and Chairman
of the Department of Management Science and Engineering, Tsinghua University. His main research interests include supply chain
management, E-commerce, decision support systems and information systems, forecast and optimization techniques. |
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| Keywords: | data mining classification cube operation relational DBMS |
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