PROVIDING TIMELY UPDATED SEQUENTIAL PATTERNS IN DECISION MAKING

Tzung-Pei Hong (), Ching-Yao Wang () and Chun-Wei Lin ()
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Tzung-Pei Hong: Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, 811, Taiwan, R.O.C.;
Ching-Yao Wang: Information & Communications Research Lab, Industrial Technology Research Institute, Hsinchu, 310, Taiwan, R.O.C.
Chun-Wei Lin: Department of Computer Science and Information Engineering, General Education Center, National University of Kaohsiung, Kaohsiung, 811, Taiwan, R.O.C.

International Journal of Information Technology & Decision Making (IJITDM), 2010, vol. 09, issue 06, 873-888

Abstract: Mining knowledge from large databases has become a critical task for organizations. Managers commonly use the obtained sequential patterns to make decisions. In the past, databases were usually assumed to be static. In real-world applications, however, transactions may be updated. In this paper, a maintenance algorithm for rapidly updating sequential patterns for real-time decision making is proposed. The proposed algorithm utilizes previously discovered large sequences in the maintenance process, thus greatly reducing the number of database rescans and improving performance. Experimental results verify the performance of the proposed approach. The proposed algorithm provides real-time knowledge that can be used for decision making.

Keywords: Data mining; sequential pattern; decision making; record modification; maintenance (search for similar items in EconPapers)
Date: 2010
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DOI: 10.1142/S0219622010004147

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