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.
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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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Liu, H., Lu, H. & Chen, J. A fast scalable classifier tightly integrated with RDBMS. J. Comput. Sci. & Technol. 17, 152–159 (2002). https://doi.org/10.1007/BF02962207
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DOI: https://doi.org/10.1007/BF02962207